format

.pre-commit-config.yaml

@@ -50,13 +50,13 @@ repos:
         entry: bash .pre-commit-hooks/clang-format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
-        exclude: (?=speechx/speechx/kaldi).*(\.cpp|\.cc|\.h|\.py)$
+        exclude: (?=speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
     -   id: copyright_checker
         name: copyright_checker
         entry: python .pre-commit-hooks/copyright-check.hook
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
-        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi).*(\.cpp|\.cc|\.h|\.py)$
+        exclude: (?=third_party|pypinyin|speechx/speechx/kaldi|speechx/patch).*(\.cpp|\.cc|\.h|\.py)$
 -   repo: https://github.com/asottile/reorder_python_imports
     rev: v2.4.0
     hooks:

paddlespeech/s2t/io/sampler.py

@@ -51,7 +51,7 @@ def _batch_shuffle(indices, batch_size, epoch, clipped=False):
     """
     rng = np.random.RandomState(epoch)
     shift_len = rng.randint(0, batch_size - 1)
-    batch_indices = list(zip(*[iter(indices[shift_len:])] * batch_size))
+    batch_indices = list(zip(* [iter(indices[shift_len:])] * batch_size))
     rng.shuffle(batch_indices)
     batch_indices = [item for batch in batch_indices for item in batch]
     assert clipped is False

paddlespeech/t2s/modules/transformer/repeat.py

@@ -36,4 +36,4 @@ def repeat(N, fn):
     Returns:
         MultiSequential: Repeated model instance.
     """
-    return MultiSequential(*[fn(n) for n in range(N)])
+    return MultiSequential(* [fn(n) for n in range(N)])

speechx/examples/README.md

@@ -3,4 +3,3 @@
 * decoder - offline decoder 
 * feat - mfcc, linear 
 * nnet - ds2 nn
-

speechx/examples/decoder/offline-decoder-main.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 // todo refactor, repalce with gtest
 
+#include "base/flags.h"
+#include "base/log.h"
 #include "decoder/ctc_beam_search_decoder.h"
 #include "kaldi/util/table-types.h"
-#include "base/log.h"
-#include "base/flags.h"
-#include "nnet/paddle_nnet.h"
 #include "nnet/decodable.h"
+#include "nnet/paddle_nnet.h"
 
 DEFINE_string(feature_respecifier, "", "test nnet prob");
 
@@ -13,45 +27,48 @@ using kaldi::BaseFloat;
 using kaldi::Matrix;
 using std::vector;
 
-//void SplitFeature(kaldi::Matrix<BaseFloat> feature, 
+// void SplitFeature(kaldi::Matrix<BaseFloat> feature,
 //                  int32 chunk_size,
 //                  std::vector<kaldi::Matrix<BaseFloat>* feature_chunks) {
 
 //}
 
 int main(int argc, char* argv[]) {
-  gflags::ParseCommandLineFlags(&argc, &argv, false);
-  google::InitGoogleLogging(argv[0]);
-  
-  kaldi::SequentialBaseFloatMatrixReader feature_reader(FLAGS_feature_respecifier);
-
-  // test nnet_output --> decoder result
-  int32 num_done = 0, num_err = 0;
-  
-  ppspeech::CTCBeamSearchOptions opts;
-  ppspeech::CTCBeamSearch decoder(opts);
-  
-  ppspeech::ModelOptions model_opts;
-  std::shared_ptr<ppspeech::PaddleNnet> nnet(new ppspeech::PaddleNnet(model_opts));
-
-  std::shared_ptr<ppspeech::Decodable> decodable(new ppspeech::Decodable(nnet));
-
-  //int32 chunk_size = 35;
-  decoder.InitDecoder();
-  for (; !feature_reader.Done(); feature_reader.Next()) {
-    string utt = feature_reader.Key();
-    const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
-    decodable->FeedFeatures(feature);
-    decoder.AdvanceDecode(decodable, 8);
-    decodable->InputFinished();
-    std::string result;
-    result = decoder.GetFinalBestPath();
-    KALDI_LOG << " the result of " << utt << " is " << result;
-    decodable->Reset();
-    ++num_done;
-  }
-
-  KALDI_LOG << "Done " << num_done << " utterances, " << num_err
-            << " with errors.";
-  return (num_done != 0 ? 0 : 1);
+    gflags::ParseCommandLineFlags(&argc, &argv, false);
+    google::InitGoogleLogging(argv[0]);
+
+    kaldi::SequentialBaseFloatMatrixReader feature_reader(
+        FLAGS_feature_respecifier);
+
+    // test nnet_output --> decoder result
+    int32 num_done = 0, num_err = 0;
+
+    ppspeech::CTCBeamSearchOptions opts;
+    ppspeech::CTCBeamSearch decoder(opts);
+
+    ppspeech::ModelOptions model_opts;
+    std::shared_ptr<ppspeech::PaddleNnet> nnet(
+        new ppspeech::PaddleNnet(model_opts));
+
+    std::shared_ptr<ppspeech::Decodable> decodable(
+        new ppspeech::Decodable(nnet));
+
+    // int32 chunk_size = 35;
+    decoder.InitDecoder();
+    for (; !feature_reader.Done(); feature_reader.Next()) {
+        string utt = feature_reader.Key();
+        const kaldi::Matrix<BaseFloat> feature = feature_reader.Value();
+        decodable->FeedFeatures(feature);
+        decoder.AdvanceDecode(decodable, 8);
+        decodable->InputFinished();
+        std::string result;
+        result = decoder.GetFinalBestPath();
+        KALDI_LOG << " the result of " << utt << " is " << result;
+        decodable->Reset();
+        ++num_done;
+    }
+
+    KALDI_LOG << "Done " << num_done << " utterances, " << num_err
+              << " with errors.";
+    return (num_done != 0 ? 0 : 1);
 }
\ No newline at end of file

speechx/examples/nnet/pp-model-test.cc

-#include "paddle_inference_api.h"
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include <gflags/gflags.h>
-#include <iostream>
-#include <thread>
+#include <algorithm>
 #include <fstream>
+#include <functional>
+#include <iostream>
 #include <iterator>
-#include <algorithm>
 #include <numeric>
-#include <functional>
+#include <thread>
+#include "paddle_inference_api.h"
 
 using std::cout;
 using std::endl;
@@ -19,27 +33,28 @@ void produce_data(std::vector<std::vector<float>>* data);
 void model_forward_test();
 
 void produce_data(std::vector<std::vector<float>>* data) {
-  int chunk_size = 35; // chunk_size in frame
-  int col_size = 161; // feat dim
-  cout << "chunk size: " << chunk_size << endl;
-  cout << "feat dim: " << col_size << endl;
-
-  data->reserve(chunk_size);
-  data->back().reserve(col_size);
-  for (int row = 0; row < chunk_size; ++row) {
-    data->push_back(std::vector<float>());
-    for (int col_idx = 0; col_idx < col_size; ++col_idx) {
-      data->back().push_back(0.201);
+    int chunk_size = 35;  // chunk_size in frame
+    int col_size = 161;   // feat dim
+    cout << "chunk size: " << chunk_size << endl;
+    cout << "feat dim: " << col_size << endl;
+
+    data->reserve(chunk_size);
+    data->back().reserve(col_size);
+    for (int row = 0; row < chunk_size; ++row) {
+        data->push_back(std::vector<float>());
+        for (int col_idx = 0; col_idx < col_size; ++col_idx) {
+            data->back().push_back(0.201);
+        }
     }
-  }
 }
 
 void model_forward_test() {
     std::cout << "1. read the data" << std::endl;
     std::vector<std::vector<float>> feats;
     produce_data(&feats);
-    
-    std::cout << "2. load the model" << std::endl;;
+
+    std::cout << "2. load the model" << std::endl;
+    ;
     std::string model_graph = FLAGS_model_path;
     std::string model_params = FLAGS_param_path;
     cout << "model path: " << model_graph << endl;
@@ -48,14 +63,15 @@ void model_forward_test() {
     paddle_infer::Config config;
     config.SetModel(model_graph, model_params);
     config.SwitchIrOptim(false);
-    cout << "SwitchIrOptim: " << false << endl; 
+    cout << "SwitchIrOptim: " << false << endl;
     config.DisableFCPadding();
     cout << "DisableFCPadding: " << endl;
     auto predictor = paddle_infer::CreatePredictor(config);
-    
-    std::cout << "3. feat shape, row=" << feats.size() << ",col=" << feats[0].size() << std::endl;
+
+    std::cout << "3. feat shape, row=" << feats.size()
+              << ",col=" << feats[0].size() << std::endl;
     std::vector<float> pp_input_mat;
-    for(const auto& item : feats) {
+    for (const auto& item : feats) {
         pp_input_mat.insert(pp_input_mat.end(), item.begin(), item.end());
     }
 
@@ -64,22 +80,23 @@ void model_forward_test() {
     int col = feats[0].size();
     std::vector<std::string> input_names = predictor->GetInputNames();
     std::vector<std::string> output_names = predictor->GetOutputNames();
-    for (auto name : input_names){
-       cout << "model input names: " <<  name << endl;
+    for (auto name : input_names) {
+        cout << "model input names: " << name << endl;
     }
-    for (auto name : output_names){
-       cout << "model output names: " << name << endl;
+    for (auto name : output_names) {
+        cout << "model output names: " << name << endl;
     }
 
     // input
-    std::unique_ptr<paddle_infer::Tensor> input_tensor = 
+    std::unique_ptr<paddle_infer::Tensor> input_tensor =
         predictor->GetInputHandle(input_names[0]);
     std::vector<int> INPUT_SHAPE = {1, row, col};
     input_tensor->Reshape(INPUT_SHAPE);
     input_tensor->CopyFromCpu(pp_input_mat.data());
-    
+
     // input length
-    std::unique_ptr<paddle_infer::Tensor> input_len = predictor->GetInputHandle(input_names[1]);
+    std::unique_ptr<paddle_infer::Tensor> input_len =
+        predictor->GetInputHandle(input_names[1]);
     std::vector<int> input_len_size = {1};
     input_len->Reshape(input_len_size);
     std::vector<int64_t> audio_len;
@@ -87,20 +104,28 @@ void model_forward_test() {
     input_len->CopyFromCpu(audio_len.data());
 
     // state_h
-    std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box = predictor->GetInputHandle(input_names[2]);
+    std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box =
+        predictor->GetInputHandle(input_names[2]);
     std::vector<int> chunk_state_h_box_shape = {3, 1, 1024};
     chunk_state_h_box->Reshape(chunk_state_h_box_shape);
-    int chunk_state_h_box_size = std::accumulate(chunk_state_h_box_shape.begin(), chunk_state_h_box_shape.end(), 
-                                    1, std::multiplies<int>());
+    int chunk_state_h_box_size =
+        std::accumulate(chunk_state_h_box_shape.begin(),
+                        chunk_state_h_box_shape.end(),
+                        1,
+                        std::multiplies<int>());
     std::vector<float> chunk_state_h_box_data(chunk_state_h_box_size, 0.0f);
     chunk_state_h_box->CopyFromCpu(chunk_state_h_box_data.data());
 
     // state_c
-    std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box = predictor->GetInputHandle(input_names[3]);
+    std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box =
+        predictor->GetInputHandle(input_names[3]);
     std::vector<int> chunk_state_c_box_shape = {3, 1, 1024};
     chunk_state_c_box->Reshape(chunk_state_c_box_shape);
-    int chunk_state_c_box_size = std::accumulate(chunk_state_c_box_shape.begin(), chunk_state_c_box_shape.end(), 
-                                    1, std::multiplies<int>());
+    int chunk_state_c_box_size =
+        std::accumulate(chunk_state_c_box_shape.begin(),
+                        chunk_state_c_box_shape.end(),
+                        1,
+                        std::multiplies<int>());
     std::vector<float> chunk_state_c_box_data(chunk_state_c_box_size, 0.0f);
     chunk_state_c_box->CopyFromCpu(chunk_state_c_box_data.data());
 
@@ -108,18 +133,20 @@ void model_forward_test() {
     bool success = predictor->Run();
 
     // state_h out
-    std::unique_ptr<paddle_infer::Tensor> h_out = predictor->GetOutputHandle(output_names[2]);
+    std::unique_ptr<paddle_infer::Tensor> h_out =
+        predictor->GetOutputHandle(output_names[2]);
     std::vector<int> h_out_shape = h_out->shape();
-    int h_out_size = std::accumulate(h_out_shape.begin(), h_out_shape.end(),
-                                      1, std::multiplies<int>());
+    int h_out_size = std::accumulate(
+        h_out_shape.begin(), h_out_shape.end(), 1, std::multiplies<int>());
     std::vector<float> h_out_data(h_out_size);
     h_out->CopyToCpu(h_out_data.data());
 
     // stage_c out
-    std::unique_ptr<paddle_infer::Tensor> c_out = predictor->GetOutputHandle(output_names[3]);
+    std::unique_ptr<paddle_infer::Tensor> c_out =
+        predictor->GetOutputHandle(output_names[3]);
     std::vector<int> c_out_shape = c_out->shape();
-    int c_out_size = std::accumulate(c_out_shape.begin(), c_out_shape.end(),
-                                      1, std::multiplies<int>());
+    int c_out_size = std::accumulate(
+        c_out_shape.begin(), c_out_shape.end(), 1, std::multiplies<int>());
     std::vector<float> c_out_data(c_out_size);
     c_out->CopyToCpu(c_out_data.data());
 
@@ -128,8 +155,8 @@ void model_forward_test() {
         predictor->GetOutputHandle(output_names[0]);
     std::vector<int> output_shape = output_tensor->shape();
     std::vector<float> output_probs;
-    int output_size = std::accumulate(output_shape.begin(), output_shape.end(),
-                                      1, std::multiplies<int>());
+    int output_size = std::accumulate(
+        output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
     output_probs.resize(output_size);
     output_tensor->CopyToCpu(output_probs.data());
     row = output_shape[1];
@@ -148,12 +175,14 @@ void model_forward_test() {
     }
 
     std::vector<std::vector<float>> log_feat = probs;
-    std::cout << "probs, row: " << log_feat.size() << " col: " << log_feat[0].size() << std::endl;
+    std::cout << "probs, row: " << log_feat.size()
+              << " col: " << log_feat[0].size() << std::endl;
     for (size_t row_idx = 0; row_idx < log_feat.size(); ++row_idx) {
-      for (size_t col_idx = 0; col_idx < log_feat[row_idx].size(); ++col_idx) {
-        std::cout << log_feat[row_idx][col_idx] << " "; 
-      }
-      std::cout << std::endl;
+        for (size_t col_idx = 0; col_idx < log_feat[row_idx].size();
+             ++col_idx) {
+            std::cout << log_feat[row_idx][col_idx] << " ";
+        }
+        std::cout << std::endl;
     }
 }
 

speechx/speechx/base/basic_types.h

@@ -18,22 +18,22 @@
 
 #include <limits>
 
-typedef float               BaseFloat;
-typedef double              double64;
+typedef float BaseFloat;
+typedef double double64;
 
-typedef signed char         int8;
-typedef short               int16;
-typedef int                 int32;
+typedef signed char int8;
+typedef short int16;
+typedef int int32;
 
 #if defined(__LP64__) && !defined(OS_MACOSX) && !defined(OS_OPENBSD)
-typedef long                int64;
+typedef long int64;
 #else
-typedef long long           int64;
+typedef long long int64;
 #endif
 
-typedef unsigned char      uint8;
-typedef unsigned short     uint16;
-typedef unsigned int       uint32;
+typedef unsigned char uint8;
+typedef unsigned short uint16;
+typedef unsigned int uint32;
 
 #if defined(__LP64__) && !defined(OS_MACOSX) && !defined(OS_OPENBSD)
 typedef unsigned long uint64;
@@ -41,20 +41,20 @@ typedef unsigned long uint64;
 typedef unsigned long long uint64;
 #endif
 
-typedef signed int         char32;
-
-const uint8  kuint8max  = (( uint8) 0xFF);
-const uint16 kuint16max = ((uint16) 0xFFFF);
-const uint32 kuint32max = ((uint32) 0xFFFFFFFF);
-const uint64 kuint64max = ((uint64) (0xFFFFFFFFFFFFFFFFLL));
-const  int8  kint8min   = ((  int8) 0x80);
-const  int8  kint8max   = ((  int8) 0x7F);
-const  int16 kint16min  = (( int16) 0x8000);
-const  int16 kint16max  = (( int16) 0x7FFF);
-const  int32 kint32min  = (( int32) 0x80000000);
-const  int32 kint32max  = (( int32) 0x7FFFFFFF);
-const  int64 kint64min  = (( int64) (0x8000000000000000LL));
-const  int64 kint64max  = (( int64) (0x7FFFFFFFFFFFFFFFLL));
-
-const  BaseFloat kBaseFloatMax = std::numeric_limits<BaseFloat>::max();
-const  BaseFloat kBaseFloatMin = std::numeric_limits<BaseFloat>::min();
+typedef signed int char32;
+
+const uint8 kuint8max = ((uint8)0xFF);
+const uint16 kuint16max = ((uint16)0xFFFF);
+const uint32 kuint32max = ((uint32)0xFFFFFFFF);
+const uint64 kuint64max = ((uint64)(0xFFFFFFFFFFFFFFFFLL));
+const int8 kint8min = ((int8)0x80);
+const int8 kint8max = ((int8)0x7F);
+const int16 kint16min = ((int16)0x8000);
+const int16 kint16max = ((int16)0x7FFF);
+const int32 kint32min = ((int32)0x80000000);
+const int32 kint32max = ((int32)0x7FFFFFFF);
+const int64 kint64min = ((int64)(0x8000000000000000LL));
+const int64 kint64max = ((int64)(0x7FFFFFFFFFFFFFFFLL));
+
+const BaseFloat kBaseFloatMax = std::numeric_limits<BaseFloat>::max();
+const BaseFloat kBaseFloatMin = std::numeric_limits<BaseFloat>::min();

speechx/speechx/base/common.h

@@ -15,22 +15,22 @@
 #pragma once
 
 #include <deque>
+#include <fstream>
 #include <iostream>
 #include <istream>
-#include <fstream>
 #include <map>
 #include <memory>
+#include <mutex>
 #include <ostream>
 #include <set>
 #include <sstream>
 #include <stack>
 #include <string>
-#include <vector>
 #include <unordered_map>
 #include <unordered_set>
-#include <mutex>
+#include <vector>
 
-#include "base/log.h"
-#include "base/flags.h"
 #include "base/basic_types.h"
+#include "base/flags.h"
+#include "base/log.h"
 #include "base/macros.h"

speechx/speechx/base/macros.h

@@ -18,8 +18,8 @@ namespace ppspeech {
 
 #ifndef DISALLOW_COPY_AND_ASSIGN
 #define DISALLOW_COPY_AND_ASSIGN(TypeName) \
-  TypeName(const TypeName&) = delete;               \
-  void operator=(const TypeName&) = delete
+    TypeName(const TypeName&) = delete;    \
+    void operator=(const TypeName&) = delete
 #endif
 
 }  // namespace pp_speech
\ No newline at end of file

speechx/speechx/base/thread_pool.h

@@ -23,98 +23,88 @@
 #ifndef BASE_THREAD_POOL_H
 #define BASE_THREAD_POOL_H
 
-#include <vector>
-#include <queue>
-#include <memory>
-#include <thread>
-#include <mutex>
 #include <condition_variable>
-#include <future>
 #include <functional>
+#include <future>
+#include <memory>
+#include <mutex>
+#include <queue>
 #include <stdexcept>
+#include <thread>
+#include <vector>
 
 class ThreadPool {
-public:
+  public:
     ThreadPool(size_t);
-    template<class F, class... Args>
-    auto enqueue(F&& f, Args&&... args) 
+    template <class F, class... Args>
+    auto enqueue(F&& f, Args&&... args)
         -> std::future<typename std::result_of<F(Args...)>::type>;
     ~ThreadPool();
-private:
+
+  private:
     // need to keep track of threads so we can join them
-    std::vector< std::thread > workers;
+    std::vector<std::thread> workers;
     // the task queue
-    std::queue< std::function<void()> > tasks;
-    
+    std::queue<std::function<void()>> tasks;
+
     // synchronization
     std::mutex queue_mutex;
     std::condition_variable condition;
     bool stop;
 };
- 
+
 // the constructor just launches some amount of workers
-inline ThreadPool::ThreadPool(size_t threads)
-    :   stop(false)
-{
-    for(size_t i = 0;i<threads;++i)
-        workers.emplace_back(
-            [this]
-            {
-                for(;;)
+inline ThreadPool::ThreadPool(size_t threads) : stop(false) {
+    for (size_t i = 0; i < threads; ++i)
+        workers.emplace_back([this] {
+            for (;;) {
+                std::function<void()> task;
+
                 {
-                    std::function<void()> task;
-
-                    {
-                        std::unique_lock<std::mutex> lock(this->queue_mutex);
-                        this->condition.wait(lock,
-                            [this]{ return this->stop || !this->tasks.empty(); });
-                        if(this->stop && this->tasks.empty())
-                            return;
-                        task = std::move(this->tasks.front());
-                        this->tasks.pop();
-                    }
-
-                    task();
+                    std::unique_lock<std::mutex> lock(this->queue_mutex);
+                    this->condition.wait(lock, [this] {
+                        return this->stop || !this->tasks.empty();
+                    });
+                    if (this->stop && this->tasks.empty()) return;
+                    task = std::move(this->tasks.front());
+                    this->tasks.pop();
                 }
+
+                task();
             }
-        );
+        });
 }
 
 // add new work item to the pool
-template<class F, class... Args>
-auto ThreadPool::enqueue(F&& f, Args&&... args) 
-    -> std::future<typename std::result_of<F(Args...)>::type>
-{
+template <class F, class... Args>
+auto ThreadPool::enqueue(F&& f, Args&&... args)
+    -> std::future<typename std::result_of<F(Args...)>::type> {
     using return_type = typename std::result_of<F(Args...)>::type;
 
-    auto task = std::make_shared< std::packaged_task<return_type()> >(
-            std::bind(std::forward<F>(f), std::forward<Args>(args)...)
-        );
-        
+    auto task = std::make_shared<std::packaged_task<return_type()>>(
+        std::bind(std::forward<F>(f), std::forward<Args>(args)...));
+
     std::future<return_type> res = task->get_future();
     {
         std::unique_lock<std::mutex> lock(queue_mutex);
 
         // don't allow enqueueing after stopping the pool
-        if(stop)
-            throw std::runtime_error("enqueue on stopped ThreadPool");
+        if (stop) throw std::runtime_error("enqueue on stopped ThreadPool");
 
-        tasks.emplace([task](){ (*task)(); });
+        tasks.emplace([task]() { (*task)(); });
     }
     condition.notify_one();
     return res;
 }
 
 // the destructor joins all threads
-inline ThreadPool::~ThreadPool()
-{
+inline ThreadPool::~ThreadPool() {
     {
         std::unique_lock<std::mutex> lock(queue_mutex);
         stop = true;
     }
     condition.notify_all();
-    for(std::thread &worker: workers)
-        worker.join();
+    for (std::thread& worker : workers) worker.join();
 }
 
 #endif

speechx/speechx/decoder/common.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "base/basic_types.h"
 
 struct DecoderResult {
-  BaseFloat acoustic_score; 
-  std::vector<int32> words_idx;
-  std::vector<pair<int32, int32>> time_stamp;
+    BaseFloat acoustic_score;
+    std::vector<int32> words_idx;
+    std::vector<pair<int32, int32>> time_stamp;
 };

speechx/speechx/decoder/ctc_beam_search_decoder.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "decoder/ctc_beam_search_decoder.h"
 
 #include "base/basic_types.h"
@@ -9,292 +23,290 @@ namespace ppspeech {
 using std::vector;
 using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
 
-CTCBeamSearch::CTCBeamSearch(const CTCBeamSearchOptions& opts) :
-    opts_(opts),
-    init_ext_scorer_(nullptr), 
-    blank_id(-1),
-    space_id(-1),
-    num_frame_decoded_(0),
-    root(nullptr) {
-
+CTCBeamSearch::CTCBeamSearch(const CTCBeamSearchOptions& opts)
+    : opts_(opts),
+      init_ext_scorer_(nullptr),
+      blank_id(-1),
+      space_id(-1),
+      num_frame_decoded_(0),
+      root(nullptr) {
     LOG(INFO) << "dict path: " << opts_.dict_file;
     if (!ReadFileToVector(opts_.dict_file, &vocabulary_)) {
         LOG(INFO) << "load the dict failed";
     }
-    LOG(INFO) << "read the vocabulary success, dict size: " << vocabulary_.size();
+    LOG(INFO) << "read the vocabulary success, dict size: "
+              << vocabulary_.size();
 
     LOG(INFO) << "language model path: " << opts_.lm_path;
-    init_ext_scorer_ = std::make_shared<Scorer>(opts_.alpha, 
-                                                opts_.beta, 
-                                                opts_.lm_path, 
-                                                vocabulary_);
+    init_ext_scorer_ = std::make_shared<Scorer>(
+        opts_.alpha, opts_.beta, opts_.lm_path, vocabulary_);
 }
 
 void CTCBeamSearch::Reset() {
-  num_frame_decoded_ = 0;
-  ResetPrefixes();
+    num_frame_decoded_ = 0;
+    ResetPrefixes();
 }
 
 void CTCBeamSearch::InitDecoder() {
-    
     blank_id = 0;
     auto it = std::find(vocabulary_.begin(), vocabulary_.end(), " ");
-    
+
     space_id = it - vocabulary_.begin();
     // if no space in vocabulary
     if ((size_t)space_id >= vocabulary_.size()) {
         space_id = -2;
-    }  
+    }
 
     ResetPrefixes();
-    
+
     root = std::make_shared<PathTrie>();
     root->score = root->log_prob_b_prev = 0.0;
     prefixes.push_back(root.get());
-    if (init_ext_scorer_ != nullptr && !init_ext_scorer_->is_character_based()) {
+    if (init_ext_scorer_ != nullptr &&
+        !init_ext_scorer_->is_character_based()) {
         auto fst_dict =
-            static_cast<fst::StdVectorFst *>(init_ext_scorer_->dictionary);
-        fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
+            static_cast<fst::StdVectorFst*>(init_ext_scorer_->dictionary);
+        fst::StdVectorFst* dict_ptr = fst_dict->Copy(true);
         root->set_dictionary(dict_ptr);
-        
+
         auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
         root->set_matcher(matcher);
     }
 }
 
-void CTCBeamSearch::Decode(std::shared_ptr<kaldi::DecodableInterface> decodable) {
-  return;
+void CTCBeamSearch::Decode(
+    std::shared_ptr<kaldi::DecodableInterface> decodable) {
+    return;
 }
 
-int32 CTCBeamSearch::NumFrameDecoded() {
-  return num_frame_decoded_;
-}
+int32 CTCBeamSearch::NumFrameDecoded() { return num_frame_decoded_; }
 
 // todo rename, refactor
-void CTCBeamSearch::AdvanceDecode(const std::shared_ptr<kaldi::DecodableInterface>& decodable,
-                                  int max_frames) {
+void CTCBeamSearch::AdvanceDecode(
+    const std::shared_ptr<kaldi::DecodableInterface>& decodable,
+    int max_frames) {
     while (max_frames > 0) {
-      vector<vector<BaseFloat>> likelihood;
-      if (decodable->IsLastFrame(NumFrameDecoded() + 1)) {
-        break;
-      }
-      likelihood.push_back(decodable->FrameLogLikelihood(NumFrameDecoded() + 1));
-      AdvanceDecoding(likelihood);
-      max_frames--;
+        vector<vector<BaseFloat>> likelihood;
+        if (decodable->IsLastFrame(NumFrameDecoded() + 1)) {
+            break;
+        }
+        likelihood.push_back(
+            decodable->FrameLogLikelihood(NumFrameDecoded() + 1));
+        AdvanceDecoding(likelihood);
+        max_frames--;
     }
 }
 
 void CTCBeamSearch::ResetPrefixes() {
-  for (size_t i = 0; i < prefixes.size(); i++) {
-    if (prefixes[i] != nullptr) {
-      delete prefixes[i];
-      prefixes[i] = nullptr;
+    for (size_t i = 0; i < prefixes.size(); i++) {
+        if (prefixes[i] != nullptr) {
+            delete prefixes[i];
+            prefixes[i] = nullptr;
+        }
     }
-  }
 }
 
-int CTCBeamSearch::DecodeLikelihoods(const vector<vector<float>>&probs, 
-                                      vector<string>& nbest_words) {
-  kaldi::Timer timer;
-  timer.Reset();
-  AdvanceDecoding(probs);
-  LOG(INFO) <<"ctc decoding elapsed time(s) " << static_cast<float>(timer.Elapsed()) / 1000.0f;
-  return 0;
-} 
+int CTCBeamSearch::DecodeLikelihoods(const vector<vector<float>>& probs,
+                                     vector<string>& nbest_words) {
+    kaldi::Timer timer;
+    timer.Reset();
+    AdvanceDecoding(probs);
+    LOG(INFO) << "ctc decoding elapsed time(s) "
+              << static_cast<float>(timer.Elapsed()) / 1000.0f;
+    return 0;
+}
 
 vector<std::pair<double, string>> CTCBeamSearch::GetNBestPath() {
-  return get_beam_search_result(prefixes, vocabulary_, opts_.beam_size);
+    return get_beam_search_result(prefixes, vocabulary_, opts_.beam_size);
 }
 
 string CTCBeamSearch::GetBestPath() {
-  std::vector<std::pair<double, std::string>> result;
-  result = get_beam_search_result(prefixes, vocabulary_, opts_.beam_size);
-  return result[0].second;
+    std::vector<std::pair<double, std::string>> result;
+    result = get_beam_search_result(prefixes, vocabulary_, opts_.beam_size);
+    return result[0].second;
 }
 
 string CTCBeamSearch::GetFinalBestPath() {
-  CalculateApproxScore();
-  LMRescore();
-  return GetBestPath();
+    CalculateApproxScore();
+    LMRescore();
+    return GetBestPath();
 }
 
 void CTCBeamSearch::AdvanceDecoding(const vector<vector<BaseFloat>>& probs) {
-  size_t num_time_steps = probs.size();
-  size_t beam_size = opts_.beam_size;
-  double cutoff_prob = opts_.cutoff_prob;
-  size_t cutoff_top_n = opts_.cutoff_top_n;
-
-  vector<vector<double>> probs_seq(probs.size(), vector<double>(probs[0].size(), 0));
-
-  int row = probs.size();
-  int col = probs[0].size();
-  for(int i = 0; i < row; i++) {
-    for (int j = 0; j < col; j++){
-      probs_seq[i][j] = static_cast<double>(probs[i][j]);
-    }
-  }
- 
-  for (size_t time_step = 0; time_step < num_time_steps; time_step++) {
-    const auto& prob = probs_seq[time_step];
-
-    float min_cutoff = -NUM_FLT_INF;
-    bool full_beam = false;
-    if (init_ext_scorer_ != nullptr) {
-      size_t num_prefixes = std::min(prefixes.size(), beam_size);
-      std::sort(prefixes.begin(), prefixes.begin() + num_prefixes,
-                prefix_compare);
-            
-      if (num_prefixes == 0) {
-        continue;
-      }
-      min_cutoff = prefixes[num_prefixes - 1]->score +
-                      std::log(prob[blank_id]) -
-                      std::max(0.0, init_ext_scorer_->beta);
-
-      full_beam = (num_prefixes == beam_size);
+    size_t num_time_steps = probs.size();
+    size_t beam_size = opts_.beam_size;
+    double cutoff_prob = opts_.cutoff_prob;
+    size_t cutoff_top_n = opts_.cutoff_top_n;
+
+    vector<vector<double>> probs_seq(probs.size(),
+                                     vector<double>(probs[0].size(), 0));
+
+    int row = probs.size();
+    int col = probs[0].size();
+    for (int i = 0; i < row; i++) {
+        for (int j = 0; j < col; j++) {
+            probs_seq[i][j] = static_cast<double>(probs[i][j]);
+        }
     }
-        
-    vector<std::pair<size_t, float>> log_prob_idx =
-                get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n);
-
-    // loop over chars
-    size_t log_prob_idx_len = log_prob_idx.size();
-    for (size_t index = 0; index < log_prob_idx_len; index++) {            
-      SearchOneChar(full_beam, log_prob_idx[index], min_cutoff);
-    } 
-
-    prefixes.clear();
-        
-    // update log probs
-    root->iterate_to_vec(prefixes);
-    // only preserve top beam_size prefixes
-    if (prefixes.size() >= beam_size) {
-      std::nth_element(prefixes.begin(),
-                       prefixes.begin() + beam_size,
-                       prefixes.end(),
-                       prefix_compare);
-      for (size_t i = beam_size; i < prefixes.size(); ++i) {
-        prefixes[i]->remove();
-      }
-    } // if 
-    num_frame_decoded_++;
-  } // for probs_seq
-}
 
-int32 CTCBeamSearch::SearchOneChar(const bool& full_beam,
-                                 const std::pair<size_t, BaseFloat>& log_prob_idx,
-                                 const BaseFloat& min_cutoff) {
-  size_t beam_size = opts_.beam_size;
-  const auto& c = log_prob_idx.first;
-  const auto& log_prob_c = log_prob_idx.second;
-  size_t prefixes_len = std::min(prefixes.size(), beam_size);
-    
-  for (size_t i = 0; i < prefixes_len; ++i) {
-    auto prefix = prefixes[i];
-    if (full_beam && log_prob_c + prefix->score < min_cutoff) {
-      break;
-    }
+    for (size_t time_step = 0; time_step < num_time_steps; time_step++) {
+        const auto& prob = probs_seq[time_step];
+
+        float min_cutoff = -NUM_FLT_INF;
+        bool full_beam = false;
+        if (init_ext_scorer_ != nullptr) {
+            size_t num_prefixes = std::min(prefixes.size(), beam_size);
+            std::sort(prefixes.begin(),
+                      prefixes.begin() + num_prefixes,
+                      prefix_compare);
+
+            if (num_prefixes == 0) {
+                continue;
+            }
+            min_cutoff = prefixes[num_prefixes - 1]->score +
+                         std::log(prob[blank_id]) -
+                         std::max(0.0, init_ext_scorer_->beta);
+
+            full_beam = (num_prefixes == beam_size);
+        }
 
-    if (c == blank_id) {
-      prefix->log_prob_b_cur = log_sum_exp(
-                              prefix->log_prob_b_cur, 
-                              log_prob_c +
-                              prefix->score);
-      continue;
-    }
+        vector<std::pair<size_t, float>> log_prob_idx =
+            get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n);
 
-    // repeated character
-    if (c == prefix->character) {
-      // p_{nb}(l;x_{1:t}) = p(c;x_{t})p(l;x_{1:t-1})
-      prefix->log_prob_nb_cur = log_sum_exp(
-                                  prefix->log_prob_nb_cur,
-                                  log_prob_c + 
-                                  prefix->log_prob_nb_prev);
-    }
+        // loop over chars
+        size_t log_prob_idx_len = log_prob_idx.size();
+        for (size_t index = 0; index < log_prob_idx_len; index++) {
+            SearchOneChar(full_beam, log_prob_idx[index], min_cutoff);
+        }
+
+        prefixes.clear();
+
+        // update log probs
+        root->iterate_to_vec(prefixes);
+        // only preserve top beam_size prefixes
+        if (prefixes.size() >= beam_size) {
+            std::nth_element(prefixes.begin(),
+                             prefixes.begin() + beam_size,
+                             prefixes.end(),
+                             prefix_compare);
+            for (size_t i = beam_size; i < prefixes.size(); ++i) {
+                prefixes[i]->remove();
+            }
+        }  // if
+        num_frame_decoded_++;
+    }  // for probs_seq
+}
 
-    // get new prefix
-    auto prefix_new = prefix->get_path_trie(c);
-    if (prefix_new != nullptr) {
-      float log_p = -NUM_FLT_INF;
-      if (c == prefix->character &&
-        prefix->log_prob_b_prev > -NUM_FLT_INF) { 
-         // p_{nb}(l^{+};x_{1:t}) = p(c;x_{t})p_{b}(l;x_{1:t-1})
-        log_p = log_prob_c + prefix->log_prob_b_prev;
-      } else if (c != prefix->character) {
-        // p_{nb}(l^{+};x_{1:t}) = p(c;x_{t}) p(l;x_{1:t-1})
-        log_p = log_prob_c + prefix->score;
-      }
-
-      // language model scoring
-      if (init_ext_scorer_ != nullptr &&
-          (c == space_id || init_ext_scorer_->is_character_based())) {
-        PathTrie *prefix_to_score = nullptr;
-        // skip scoring the space
-        if (init_ext_scorer_->is_character_based()) {
-            prefix_to_score = prefix_new;
-        } else {
-            prefix_to_score = prefix;
+int32 CTCBeamSearch::SearchOneChar(
+    const bool& full_beam,
+    const std::pair<size_t, BaseFloat>& log_prob_idx,
+    const BaseFloat& min_cutoff) {
+    size_t beam_size = opts_.beam_size;
+    const auto& c = log_prob_idx.first;
+    const auto& log_prob_c = log_prob_idx.second;
+    size_t prefixes_len = std::min(prefixes.size(), beam_size);
+
+    for (size_t i = 0; i < prefixes_len; ++i) {
+        auto prefix = prefixes[i];
+        if (full_beam && log_prob_c + prefix->score < min_cutoff) {
+            break;
         }
 
-        float score = 0.0;
-        vector<string> ngram;
-        ngram = init_ext_scorer_->make_ngram(prefix_to_score);
-        // lm score: p_{lm}(W)^{\alpha} + \beta
-        score = init_ext_scorer_->get_log_cond_prob(ngram) *
-                                init_ext_scorer_->alpha;
-        log_p += score;
-        log_p += init_ext_scorer_->beta;
-      }
-      // p_{nb}(l;x_{1:t})
-      prefix_new->log_prob_nb_cur =
-                      log_sum_exp(prefix_new->log_prob_nb_cur, 
-                                  log_p);
-    }
-  } // end of loop over prefix
-  return 0;
+        if (c == blank_id) {
+            prefix->log_prob_b_cur =
+                log_sum_exp(prefix->log_prob_b_cur, log_prob_c + prefix->score);
+            continue;
+        }
+
+        // repeated character
+        if (c == prefix->character) {
+            // p_{nb}(l;x_{1:t}) = p(c;x_{t})p(l;x_{1:t-1})
+            prefix->log_prob_nb_cur = log_sum_exp(
+                prefix->log_prob_nb_cur, log_prob_c + prefix->log_prob_nb_prev);
+        }
+
+        // get new prefix
+        auto prefix_new = prefix->get_path_trie(c);
+        if (prefix_new != nullptr) {
+            float log_p = -NUM_FLT_INF;
+            if (c == prefix->character &&
+                prefix->log_prob_b_prev > -NUM_FLT_INF) {
+                // p_{nb}(l^{+};x_{1:t}) = p(c;x_{t})p_{b}(l;x_{1:t-1})
+                log_p = log_prob_c + prefix->log_prob_b_prev;
+            } else if (c != prefix->character) {
+                // p_{nb}(l^{+};x_{1:t}) = p(c;x_{t}) p(l;x_{1:t-1})
+                log_p = log_prob_c + prefix->score;
+            }
+
+            // language model scoring
+            if (init_ext_scorer_ != nullptr &&
+                (c == space_id || init_ext_scorer_->is_character_based())) {
+                PathTrie* prefix_to_score = nullptr;
+                // skip scoring the space
+                if (init_ext_scorer_->is_character_based()) {
+                    prefix_to_score = prefix_new;
+                } else {
+                    prefix_to_score = prefix;
+                }
+
+                float score = 0.0;
+                vector<string> ngram;
+                ngram = init_ext_scorer_->make_ngram(prefix_to_score);
+                // lm score: p_{lm}(W)^{\alpha} + \beta
+                score = init_ext_scorer_->get_log_cond_prob(ngram) *
+                        init_ext_scorer_->alpha;
+                log_p += score;
+                log_p += init_ext_scorer_->beta;
+            }
+            // p_{nb}(l;x_{1:t})
+            prefix_new->log_prob_nb_cur =
+                log_sum_exp(prefix_new->log_prob_nb_cur, log_p);
+        }
+    }  // end of loop over prefix
+    return 0;
 }
 
 void CTCBeamSearch::CalculateApproxScore() {
-  size_t beam_size = opts_.beam_size;
-  size_t num_prefixes = std::min(prefixes.size(), beam_size);
-  std::sort(
-      prefixes.begin(), 
-      prefixes.begin() + num_prefixes, 
-      prefix_compare);
-
-  // compute aproximate ctc score as the return score, without affecting the
-  // return order of decoding result. To delete when decoder gets stable.
-  for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
-    double approx_ctc = prefixes[i]->score;
-    if (init_ext_scorer_ != nullptr) {
-      vector<int> output;
-      prefixes[i]->get_path_vec(output);
-      auto prefix_length = output.size();
-      auto words = init_ext_scorer_->split_labels(output);
-      // remove word insert
-      approx_ctc = approx_ctc - prefix_length * init_ext_scorer_->beta;
-      // remove language model weight:
-      approx_ctc -=
-          (init_ext_scorer_->get_sent_log_prob(words)) * init_ext_scorer_->alpha;
+    size_t beam_size = opts_.beam_size;
+    size_t num_prefixes = std::min(prefixes.size(), beam_size);
+    std::sort(
+        prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
+
+    // compute aproximate ctc score as the return score, without affecting the
+    // return order of decoding result. To delete when decoder gets stable.
+    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
+        double approx_ctc = prefixes[i]->score;
+        if (init_ext_scorer_ != nullptr) {
+            vector<int> output;
+            prefixes[i]->get_path_vec(output);
+            auto prefix_length = output.size();
+            auto words = init_ext_scorer_->split_labels(output);
+            // remove word insert
+            approx_ctc = approx_ctc - prefix_length * init_ext_scorer_->beta;
+            // remove language model weight:
+            approx_ctc -= (init_ext_scorer_->get_sent_log_prob(words)) *
+                          init_ext_scorer_->alpha;
+        }
+        prefixes[i]->approx_ctc = approx_ctc;
     }
-    prefixes[i]->approx_ctc = approx_ctc;
-  }
 }
 
 void CTCBeamSearch::LMRescore() {
-  size_t beam_size = opts_.beam_size;
-  if (init_ext_scorer_ != nullptr && !init_ext_scorer_->is_character_based()) {
-    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
-      auto prefix = prefixes[i];
-      if (!prefix->is_empty() && prefix->character != space_id) {
-        float score = 0.0;
-        vector<string> ngram = init_ext_scorer_->make_ngram(prefix);
-        score = init_ext_scorer_->get_log_cond_prob(ngram) * init_ext_scorer_->alpha;
-        score += init_ext_scorer_->beta;
-        prefix->score += score;
-      }
+    size_t beam_size = opts_.beam_size;
+    if (init_ext_scorer_ != nullptr &&
+        !init_ext_scorer_->is_character_based()) {
+        for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
+            auto prefix = prefixes[i];
+            if (!prefix->is_empty() && prefix->character != space_id) {
+                float score = 0.0;
+                vector<string> ngram = init_ext_scorer_->make_ngram(prefix);
+                score = init_ext_scorer_->get_log_cond_prob(ngram) *
+                        init_ext_scorer_->alpha;
+                score += init_ext_scorer_->beta;
+                prefix->score += score;
+            }
+        }
     }
-  }
 }
 
-} // namespace ppspeech
\ No newline at end of file
+}  // namespace ppspeech
\ No newline at end of file

speechx/speechx/decoder/ctc_beam_search_decoder.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "base/common.h"
+#include "decoder/ctc_decoders/path_trie.h"
+#include "decoder/ctc_decoders/scorer.h"
 #include "nnet/decodable-itf.h"
 #include "util/parse-options.h"
-#include "decoder/ctc_decoders/scorer.h"
-#include "decoder/ctc_decoders/path_trie.h"
 
 #pragma once
 
@@ -17,63 +31,66 @@ struct CTCBeamSearchOptions {
     int beam_size;
     int cutoff_top_n;
     int num_proc_bsearch;
-    CTCBeamSearchOptions() :
-        dict_file("./model/words.txt"),
-        lm_path("./model/lm.arpa"),
-        alpha(1.9f),
-        beta(5.0),
-        beam_size(300),
-        cutoff_prob(0.99f),
-        cutoff_top_n(40),
-        num_proc_bsearch(0) {
-    }
+    CTCBeamSearchOptions()
+        : dict_file("./model/words.txt"),
+          lm_path("./model/lm.arpa"),
+          alpha(1.9f),
+          beta(5.0),
+          beam_size(300),
+          cutoff_prob(0.99f),
+          cutoff_top_n(40),
+          num_proc_bsearch(0) {}
 
     void Register(kaldi::OptionsItf* opts) {
         opts->Register("dict", &dict_file, "dict file ");
         opts->Register("lm-path", &lm_path, "language model file");
         opts->Register("alpha", &alpha, "alpha");
         opts->Register("beta", &beta, "beta");
-        opts->Register("beam-size", &beam_size, "beam size for beam search method");
+        opts->Register(
+            "beam-size", &beam_size, "beam size for beam search method");
         opts->Register("cutoff-prob", &cutoff_prob, "cutoff probs");
         opts->Register("cutoff-top-n", &cutoff_top_n, "cutoff top n");
-        opts->Register("num-proc-bsearch", &num_proc_bsearch, "num proc bsearch");
+        opts->Register(
+            "num-proc-bsearch", &num_proc_bsearch, "num proc bsearch");
     }
 };
 
 class CTCBeamSearch {
- public: 
+  public:
     explicit CTCBeamSearch(const CTCBeamSearchOptions& opts);
     ~CTCBeamSearch() {}
     void InitDecoder();
     void Decode(std::shared_ptr<kaldi::DecodableInterface> decodable);
-    std::string GetBestPath(); 
-    std::vector<std::pair<double, std::string>> GetNBestPath(); 
+    std::string GetBestPath();
+    std::vector<std::pair<double, std::string>> GetNBestPath();
     std::string GetFinalBestPath();
     int NumFrameDecoded();
-    int DecodeLikelihoods(const std::vector<std::vector<BaseFloat>>&probs, 
+    int DecodeLikelihoods(const std::vector<std::vector<BaseFloat>>& probs,
                           std::vector<std::string>& nbest_words);
-    void AdvanceDecode(const std::shared_ptr<kaldi::DecodableInterface>& decodable,
-                       int max_frames);
+    void AdvanceDecode(
+        const std::shared_ptr<kaldi::DecodableInterface>& decodable,
+        int max_frames);
     void Reset();
- private:
-  void ResetPrefixes();
-  int32 SearchOneChar(const bool& full_beam,
-                      const std::pair<size_t, BaseFloat>& log_prob_idx,
-                      const BaseFloat& min_cutoff);
-  void CalculateApproxScore();
-  void LMRescore();
-  void AdvanceDecoding(const std::vector<std::vector<BaseFloat>>& probs);
-  
-  CTCBeamSearchOptions opts_;
-  std::shared_ptr<Scorer> init_ext_scorer_; // todo separate later
-  //std::vector<DecodeResult> decoder_results_;
-  std::vector<std::string> vocabulary_; // todo remove later
-  size_t blank_id;        
-  int space_id;
-  std::shared_ptr<PathTrie> root;
-  std::vector<PathTrie*> prefixes;
-  int num_frame_decoded_;
-  DISALLOW_COPY_AND_ASSIGN(CTCBeamSearch);
+
+  private:
+    void ResetPrefixes();
+    int32 SearchOneChar(const bool& full_beam,
+                        const std::pair<size_t, BaseFloat>& log_prob_idx,
+                        const BaseFloat& min_cutoff);
+    void CalculateApproxScore();
+    void LMRescore();
+    void AdvanceDecoding(const std::vector<std::vector<BaseFloat>>& probs);
+
+    CTCBeamSearchOptions opts_;
+    std::shared_ptr<Scorer> init_ext_scorer_;  // todo separate later
+    // std::vector<DecodeResult> decoder_results_;
+    std::vector<std::string> vocabulary_;  // todo remove later
+    size_t blank_id;
+    int space_id;
+    std::shared_ptr<PathTrie> root;
+    std::vector<PathTrie*> prefixes;
+    int num_frame_decoded_;
+    DISALLOW_COPY_AND_ASSIGN(CTCBeamSearch);
 };
 
-} // namespace basr
\ No newline at end of file
+}  // namespace basr
\ No newline at end of file

speechx/speechx/frontend/fbank.h

@@ -22,15 +22,16 @@ namespace ppspeech {
 
 class FbankExtractor : FeatureExtractorInterface {
   public:
-    explicit FbankExtractor(const FbankOptions& opts, 
+    explicit FbankExtractor(const FbankOptions& opts,
                             share_ptr<FeatureExtractorInterface> pre_extractor);
-    virtual void AcceptWaveform(const kaldi::Vector<kaldi::BaseFloat>& input) = 0;
+    virtual void AcceptWaveform(
+        const kaldi::Vector<kaldi::BaseFloat>& input) = 0;
     virtual void Read(kaldi::Vector<kaldi::BaseFloat>* feat) = 0;
     virtual size_t Dim() const = 0;
 
   private:
-     bool Compute(const kaldi::Vector<kaldi::BaseFloat>& wave,
-                  kaldi::Vector<kaldi::BaseFloat>* feat) const; 
+    bool Compute(const kaldi::Vector<kaldi::BaseFloat>& wave,
+                 kaldi::Vector<kaldi::BaseFloat>* feat) const;
 };
 
 }  // namespace ppspeech
\ No newline at end of file

speechx/speechx/frontend/feature_extractor_controller.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+

speechx/speechx/frontend/feature_extractor_controller_impl.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+

speechx/speechx/frontend/feature_extractor_interface.h

@@ -21,7 +21,8 @@ namespace ppspeech {
 
 class FeatureExtractorInterface {
   public:
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) = 0;
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input) = 0;
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat) = 0;
     virtual size_t Dim() const = 0;
 };

speechx/speechx/frontend/linear_spectrogram.cc

@@ -25,97 +25,97 @@ using kaldi::VectorBase;
 using kaldi::Matrix;
 using std::vector;
 
-//todo remove later
+// todo remove later
 void CopyVector2StdVector_(const VectorBase<BaseFloat>& input,
-                          vector<BaseFloat>* output) {
-  if (input.Dim() == 0) return;
-  output->resize(input.Dim());
-  for (size_t idx = 0; idx < input.Dim(); ++idx) {
-    (*output)[idx] = input(idx);
-  }
+                           vector<BaseFloat>* output) {
+    if (input.Dim() == 0) return;
+    output->resize(input.Dim());
+    for (size_t idx = 0; idx < input.Dim(); ++idx) {
+        (*output)[idx] = input(idx);
+    }
 }
 
 void CopyStdVector2Vector_(const vector<BaseFloat>& input,
-                          Vector<BaseFloat>* output) {
-  if (input.empty()) return;
-  output->Resize(input.size());
-  for (size_t idx = 0; idx < input.size(); ++idx) {
-    (*output)(idx) = input[idx];
-  }
+                           Vector<BaseFloat>* output) {
+    if (input.empty()) return;
+    output->Resize(input.size());
+    for (size_t idx = 0; idx < input.size(); ++idx) {
+        (*output)(idx) = input[idx];
+    }
 }
 
 LinearSpectrogram::LinearSpectrogram(
     const LinearSpectrogramOptions& opts,
     std::unique_ptr<FeatureExtractorInterface> base_extractor) {
-  opts_ = opts;
-  base_extractor_ = std::move(base_extractor);
-  int32 window_size = opts.frame_opts.WindowSize();
-  int32 window_shift = opts.frame_opts.WindowShift();
-  fft_points_ = window_size;
-  hanning_window_.resize(window_size);
-
-  double a = M_2PI / (window_size - 1);
-  hanning_window_energy_ = 0;
-  for (int i = 0; i < window_size; ++i) {
-    hanning_window_[i] = 0.5 - 0.5 * cos(a * i);
-    hanning_window_energy_ += hanning_window_[i] * hanning_window_[i];
-  }
-
-  dim_ = fft_points_ / 2 + 1;  // the dimension is Fs/2 Hz
+    opts_ = opts;
+    base_extractor_ = std::move(base_extractor);
+    int32 window_size = opts.frame_opts.WindowSize();
+    int32 window_shift = opts.frame_opts.WindowShift();
+    fft_points_ = window_size;
+    hanning_window_.resize(window_size);
+
+    double a = M_2PI / (window_size - 1);
+    hanning_window_energy_ = 0;
+    for (int i = 0; i < window_size; ++i) {
+        hanning_window_[i] = 0.5 - 0.5 * cos(a * i);
+        hanning_window_energy_ += hanning_window_[i] * hanning_window_[i];
+    }
+
+    dim_ = fft_points_ / 2 + 1;  // the dimension is Fs/2 Hz
 }
 
 void LinearSpectrogram::AcceptWaveform(const VectorBase<BaseFloat>& input) {
-  base_extractor_->AcceptWaveform(input);
+    base_extractor_->AcceptWaveform(input);
 }
 
 void LinearSpectrogram::Hanning(vector<float>* data) const {
-  CHECK_GE(data->size(), hanning_window_.size());
+    CHECK_GE(data->size(), hanning_window_.size());
 
-  for (size_t i = 0; i < hanning_window_.size(); ++i) {
-      data->at(i) *= hanning_window_[i];
-  }
+    for (size_t i = 0; i < hanning_window_.size(); ++i) {
+        data->at(i) *= hanning_window_[i];
+    }
 }
 
 bool LinearSpectrogram::NumpyFft(vector<BaseFloat>* v,
                                  vector<BaseFloat>* real,
                                  vector<BaseFloat>* img) const {
-  Vector<BaseFloat> v_tmp;
-  CopyStdVector2Vector_(*v, &v_tmp);
-  RealFft(&v_tmp, true);
-  CopyVector2StdVector_(v_tmp, v);
-  real->push_back(v->at(0));
-  img->push_back(0);
-  for (int i = 1; i < v->size() / 2; i++) {
-    real->push_back(v->at(2 * i));
-    img->push_back(v->at(2 * i + 1));
-  }
-  real->push_back(v->at(1));
-  img->push_back(0);
-
-  return true;
+    Vector<BaseFloat> v_tmp;
+    CopyStdVector2Vector_(*v, &v_tmp);
+    RealFft(&v_tmp, true);
+    CopyVector2StdVector_(v_tmp, v);
+    real->push_back(v->at(0));
+    img->push_back(0);
+    for (int i = 1; i < v->size() / 2; i++) {
+        real->push_back(v->at(2 * i));
+        img->push_back(v->at(2 * i + 1));
+    }
+    real->push_back(v->at(1));
+    img->push_back(0);
+
+    return true;
 }
 
 // todo remove later
 void LinearSpectrogram::ReadFeats(Matrix<BaseFloat>* feats) {
-  Vector<BaseFloat> tmp;
-  waveform_.Resize(base_extractor_->Dim());
-  Compute(tmp, &waveform_);
-  vector<vector<BaseFloat>> result;
-  vector<BaseFloat> feats_vec; 
-  CopyVector2StdVector_(waveform_, &feats_vec);
-  Compute(feats_vec, result);
-  feats->Resize(result.size(), result[0].size());
-  for (int row_idx = 0; row_idx < result.size(); ++row_idx) {
-    for (int col_idx = 0; col_idx < result[0].size(); ++col_idx) {
-        (*feats)(row_idx, col_idx) = result[row_idx][col_idx];
+    Vector<BaseFloat> tmp;
+    waveform_.Resize(base_extractor_->Dim());
+    Compute(tmp, &waveform_);
+    vector<vector<BaseFloat>> result;
+    vector<BaseFloat> feats_vec;
+    CopyVector2StdVector_(waveform_, &feats_vec);
+    Compute(feats_vec, result);
+    feats->Resize(result.size(), result[0].size());
+    for (int row_idx = 0; row_idx < result.size(); ++row_idx) {
+        for (int col_idx = 0; col_idx < result[0].size(); ++col_idx) {
+            (*feats)(row_idx, col_idx) = result[row_idx][col_idx];
+        }
     }
-  }
-  waveform_.Resize(0);
+    waveform_.Resize(0);
 }
 
 void LinearSpectrogram::Read(VectorBase<BaseFloat>* feat) {
-  // todo
-  return;
+    // todo
+    return;
 }
 
 // only for test, remove later
@@ -129,49 +129,49 @@ void LinearSpectrogram::Compute(const VectorBase<kaldi::BaseFloat>& input,
 // todo: refactor later (SmileGoat)
 bool LinearSpectrogram::Compute(const vector<float>& wave,
                                 vector<vector<float>>& feat) {
-  int num_samples = wave.size();
-  const int& frame_length = opts_.frame_opts.WindowSize();
-  const int& sample_rate = opts_.frame_opts.samp_freq;
-  const int& frame_shift = opts_.frame_opts.WindowShift();
-  const int& fft_points = fft_points_;
-  const float scale = hanning_window_energy_ * sample_rate;
-
-  if (num_samples < frame_length) {
-          return true;
-  }
-
-  int num_frames = 1 + ((num_samples - frame_length) / frame_shift);
-  feat.resize(num_frames);
-  vector<float> fft_real((fft_points_ / 2 + 1), 0);
-  vector<float> fft_img((fft_points_ / 2 + 1), 0);
-  vector<float> v(frame_length, 0);
-  vector<float> power((fft_points / 2 + 1));
-
-  for (int i = 0; i < num_frames; ++i) {
-    vector<float> data(wave.data() + i * frame_shift,
-                       wave.data() + i * frame_shift + frame_length);
-    Hanning(&data);
-    fft_img.clear();
-    fft_real.clear();
-    v.assign(data.begin(), data.end());
-    NumpyFft(&v, &fft_real, &fft_img);
-
-    feat[i].resize(fft_points / 2 + 1);  // the last dimension is Fs/2 Hz
-    for (int j = 0; j < (fft_points / 2 + 1); ++j) {
-      power[j] = fft_real[j] * fft_real[j] + fft_img[j] * fft_img[j];
-      feat[i][j] = power[j];
-
-      if (j == 0 || j == feat[0].size() - 1) {
-          feat[i][j] /= scale;
-      } else {
-          feat[i][j] *= (2.0 / scale);
-      }
-
-      // log added eps=1e-14
-      feat[i][j] = std::log(feat[i][j] + 1e-14);
+    int num_samples = wave.size();
+    const int& frame_length = opts_.frame_opts.WindowSize();
+    const int& sample_rate = opts_.frame_opts.samp_freq;
+    const int& frame_shift = opts_.frame_opts.WindowShift();
+    const int& fft_points = fft_points_;
+    const float scale = hanning_window_energy_ * sample_rate;
+
+    if (num_samples < frame_length) {
+        return true;
+    }
+
+    int num_frames = 1 + ((num_samples - frame_length) / frame_shift);
+    feat.resize(num_frames);
+    vector<float> fft_real((fft_points_ / 2 + 1), 0);
+    vector<float> fft_img((fft_points_ / 2 + 1), 0);
+    vector<float> v(frame_length, 0);
+    vector<float> power((fft_points / 2 + 1));
+
+    for (int i = 0; i < num_frames; ++i) {
+        vector<float> data(wave.data() + i * frame_shift,
+                           wave.data() + i * frame_shift + frame_length);
+        Hanning(&data);
+        fft_img.clear();
+        fft_real.clear();
+        v.assign(data.begin(), data.end());
+        NumpyFft(&v, &fft_real, &fft_img);
+
+        feat[i].resize(fft_points / 2 + 1);  // the last dimension is Fs/2 Hz
+        for (int j = 0; j < (fft_points / 2 + 1); ++j) {
+            power[j] = fft_real[j] * fft_real[j] + fft_img[j] * fft_img[j];
+            feat[i][j] = power[j];
+
+            if (j == 0 || j == feat[0].size() - 1) {
+                feat[i][j] /= scale;
+            } else {
+                feat[i][j] *= (2.0 / scale);
+            }
+
+            // log added eps=1e-14
+            feat[i][j] = std::log(feat[i][j] + 1e-14);
+        }
     }
-  }
-  return true;
+    return true;
 }
 
 }  // namespace ppspeech
\ No newline at end of file

speechx/speechx/frontend/linear_spectrogram.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 
 #pragma once
 
+#include "base/common.h"
 #include "frontend/feature_extractor_interface.h"
 #include "kaldi/feat/feature-window.h"
-#include "base/common.h"
 
 namespace ppspeech {
 
 struct LinearSpectrogramOptions {
     kaldi::FrameExtractionOptions frame_opts;
-    LinearSpectrogramOptions():
-        frame_opts() {}
+    LinearSpectrogramOptions() : frame_opts() {}
 
-    void Register(kaldi::OptionsItf* opts) {
-        frame_opts.Register(opts);
-    }
+    void Register(kaldi::OptionsItf* opts) { frame_opts.Register(opts); }
 };
 
 class LinearSpectrogram : public FeatureExtractorInterface {
   public:
-    explicit LinearSpectrogram(const LinearSpectrogramOptions& opts,
-                               std::unique_ptr<FeatureExtractorInterface> base_extractor);
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    explicit LinearSpectrogram(
+        const LinearSpectrogramOptions& opts,
+        std::unique_ptr<FeatureExtractorInterface> base_extractor);
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input);
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return dim_; }
     void ReadFeats(kaldi::Matrix<kaldi::BaseFloat>* feats);
 
-  private: 
+  private:
     void Hanning(std::vector<kaldi::BaseFloat>* data) const;
     bool Compute(const std::vector<kaldi::BaseFloat>& wave,
                  std::vector<std::vector<kaldi::BaseFloat>>& feat);
@@ -41,7 +54,7 @@ class LinearSpectrogram : public FeatureExtractorInterface {
     std::vector<kaldi::BaseFloat> hanning_window_;
     kaldi::BaseFloat hanning_window_energy_;
     LinearSpectrogramOptions opts_;
-    kaldi::Vector<kaldi::BaseFloat> waveform_; // remove later, todo(SmileGoat)
+    kaldi::Vector<kaldi::BaseFloat> waveform_;  // remove later, todo(SmileGoat)
     std::unique_ptr<FeatureExtractorInterface> base_extractor_;
     DISALLOW_COPY_AND_ASSIGN(LinearSpectrogram);
 };

speechx/speechx/frontend/normalizer.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 
 #include "frontend/normalizer.h"
 #include "kaldi/feat/cmvn.h"
@@ -12,169 +26,173 @@ using std::vector;
 using kaldi::SubVector;
 
 DecibelNormalizer::DecibelNormalizer(const DecibelNormalizerOptions& opts) {
-  opts_ = opts;
-  dim_ = 0;
+    opts_ = opts;
+    dim_ = 0;
 }
-                                    
-void DecibelNormalizer::AcceptWaveform(const kaldi::VectorBase<BaseFloat>& input) {
-  dim_ = input.Dim();
-  waveform_.Resize(input.Dim());
-  waveform_.CopyFromVec(input);
+
+void DecibelNormalizer::AcceptWaveform(
+    const kaldi::VectorBase<BaseFloat>& input) {
+    dim_ = input.Dim();
+    waveform_.Resize(input.Dim());
+    waveform_.CopyFromVec(input);
 }
 
 void DecibelNormalizer::Read(kaldi::VectorBase<BaseFloat>* feat) {
-  if (waveform_.Dim() == 0) return;
-  Compute(waveform_, feat);
+    if (waveform_.Dim() == 0) return;
+    Compute(waveform_, feat);
 }
 
-//todo remove later
+// todo remove later
 void CopyVector2StdVector(const kaldi::VectorBase<BaseFloat>& input,
                           vector<BaseFloat>* output) {
-  if (input.Dim() == 0) return;
-  output->resize(input.Dim());
-  for (size_t idx = 0; idx < input.Dim(); ++idx) {
-    (*output)[idx] = input(idx);
-  }
+    if (input.Dim() == 0) return;
+    output->resize(input.Dim());
+    for (size_t idx = 0; idx < input.Dim(); ++idx) {
+        (*output)[idx] = input(idx);
+    }
 }
 
 void CopyStdVector2Vector(const vector<BaseFloat>& input,
                           VectorBase<BaseFloat>* output) {
-  if (input.empty()) return;
-  assert(input.size() == output->Dim());
-  for (size_t idx = 0; idx < input.size(); ++idx) {
-    (*output)(idx) = input[idx];
-  }
+    if (input.empty()) return;
+    assert(input.size() == output->Dim());
+    for (size_t idx = 0; idx < input.size(); ++idx) {
+        (*output)(idx) = input[idx];
+    }
 }
 
 bool DecibelNormalizer::Compute(const VectorBase<BaseFloat>& input,
                                 VectorBase<BaseFloat>* feat) const {
-  // calculate db rms
-  BaseFloat rms_db = 0.0;
-  BaseFloat mean_square = 0.0;
-  BaseFloat gain = 0.0;
-  BaseFloat wave_float_normlization = 1.0f / (std::pow(2, 16 - 1));
-
-  vector<BaseFloat> samples;
-  samples.resize(input.Dim());
-  for (int32 i = 0; i < samples.size(); ++i) {
-    samples[i] = input(i);
-  }
-  
-  // square
-  for (auto &d : samples) {
-    if (opts_.convert_int_float) {
-    d = d * wave_float_normlization;
+    // calculate db rms
+    BaseFloat rms_db = 0.0;
+    BaseFloat mean_square = 0.0;
+    BaseFloat gain = 0.0;
+    BaseFloat wave_float_normlization = 1.0f / (std::pow(2, 16 - 1));
+
+    vector<BaseFloat> samples;
+    samples.resize(input.Dim());
+    for (int32 i = 0; i < samples.size(); ++i) {
+        samples[i] = input(i);
     }
-    mean_square += d * d;
-  }
-
-  // mean
-  mean_square /= samples.size();
-  rms_db = 10 * std::log10(mean_square);
-  gain = opts_.target_db - rms_db;
-
-  if (gain > opts_.max_gain_db) {
-    LOG(ERROR) << "Unable to normalize segment to " << opts_.target_db << "dB,"
-                << "because the the probable gain have exceeds opts_.max_gain_db" 
-                <<  opts_.max_gain_db << "dB.";
-    return false;
-  }
-
-  // Note that this is an in-place transformation.
-  for (auto &item : samples) {
-    // python item *= 10.0 ** (gain / 20.0)
-    item *= std::pow(10.0, gain / 20.0);
-  }
-  
-  CopyStdVector2Vector(samples, feat);
-  return true;
+
+    // square
+    for (auto& d : samples) {
+        if (opts_.convert_int_float) {
+            d = d * wave_float_normlization;
+        }
+        mean_square += d * d;
+    }
+
+    // mean
+    mean_square /= samples.size();
+    rms_db = 10 * std::log10(mean_square);
+    gain = opts_.target_db - rms_db;
+
+    if (gain > opts_.max_gain_db) {
+        LOG(ERROR)
+            << "Unable to normalize segment to " << opts_.target_db << "dB,"
+            << "because the the probable gain have exceeds opts_.max_gain_db"
+            << opts_.max_gain_db << "dB.";
+        return false;
+    }
+
+    // Note that this is an in-place transformation.
+    for (auto& item : samples) {
+        // python item *= 10.0 ** (gain / 20.0)
+        item *= std::pow(10.0, gain / 20.0);
+    }
+
+    CopyStdVector2Vector(samples, feat);
+    return true;
 }
 
 CMVN::CMVN(std::string cmvn_file) : var_norm_(true) {
-  bool binary;
-  kaldi::Input ki(cmvn_file, &binary);
-  stats_.Read(ki.Stream(), binary);
+    bool binary;
+    kaldi::Input ki(cmvn_file, &binary);
+    stats_.Read(ki.Stream(), binary);
 }
 
 void CMVN::AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input) {
-  return;
+    return;
 }
 
-void CMVN::Read(kaldi::VectorBase<BaseFloat>* feat) {
-  return;
-}
+void CMVN::Read(kaldi::VectorBase<BaseFloat>* feat) { return; }
 
 // feats contain num_frames feature.
 void CMVN::ApplyCMVN(bool var_norm, VectorBase<BaseFloat>* feats) {
-  KALDI_ASSERT(feats != NULL); 
-  int32 dim = stats_.NumCols() - 1;
-  if (stats_.NumRows() > 2 || stats_.NumRows() < 1 || feats->Dim() % dim != 0) {
-    KALDI_ERR << "Dim mismatch: cmvn "
-              << stats_.NumRows() << 'x' << stats_.NumCols()
-              << ", feats " << feats->Dim() << 'x';
-  }
-  if (stats_.NumRows() == 1 && var_norm) {
-    KALDI_ERR << "You requested variance normalization but no variance stats_ "
-              << "are supplied.";
-  }
-
-  double count = stats_(0, dim);
-  // Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
-  // computing an offset and representing it as stats_, we use a count of one.
-  if (count < 1.0)
-    KALDI_ERR << "Insufficient stats_ for cepstral mean and variance normalization: "
-              << "count = " << count;
-
-  if (!var_norm) {
-    Vector<BaseFloat> offset(feats->Dim());
-    SubVector<double> mean_stats(stats_.RowData(0), dim);
-    Vector<double> mean_stats_apply(feats->Dim());
-    //fill the datat of mean_stats in mean_stats_appy whose dim is equal with the dim of feature.
-    //the dim of feats = dim * num_frames;
-    for (int32 idx = 0; idx < feats->Dim() / dim; ++idx) {
-      SubVector<double> stats_tmp(mean_stats_apply.Data() + dim*idx, dim);
-      stats_tmp.CopyFromVec(mean_stats);
+    KALDI_ASSERT(feats != NULL);
+    int32 dim = stats_.NumCols() - 1;
+    if (stats_.NumRows() > 2 || stats_.NumRows() < 1 ||
+        feats->Dim() % dim != 0) {
+        KALDI_ERR << "Dim mismatch: cmvn " << stats_.NumRows() << 'x'
+                  << stats_.NumCols() << ", feats " << feats->Dim() << 'x';
     }
-    offset.AddVec(-1.0 / count, mean_stats_apply);
-    feats->AddVec(1.0, offset);
-    return;
-  }
-  // norm(0, d) = mean offset;
-  // norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
-  kaldi::Matrix<BaseFloat> norm(2, feats->Dim());
-  for (int32 d = 0; d < dim; d++) {
-    double mean, offset, scale;
-    mean = stats_(0, d)/count;
-    double var = (stats_(1, d)/count) - mean*mean,
-        floor = 1.0e-20;
-    if (var < floor) {
-      KALDI_WARN << "Flooring cepstral variance from " << var << " to "
-                 << floor;
-      var = floor;
+    if (stats_.NumRows() == 1 && var_norm) {
+        KALDI_ERR
+            << "You requested variance normalization but no variance stats_ "
+            << "are supplied.";
+    }
+
+    double count = stats_(0, dim);
+    // Do not change the threshold of 1.0 here: in the balanced-cmvn code, when
+    // computing an offset and representing it as stats_, we use a count of one.
+    if (count < 1.0)
+        KALDI_ERR << "Insufficient stats_ for cepstral mean and variance "
+                     "normalization: "
+                  << "count = " << count;
+
+    if (!var_norm) {
+        Vector<BaseFloat> offset(feats->Dim());
+        SubVector<double> mean_stats(stats_.RowData(0), dim);
+        Vector<double> mean_stats_apply(feats->Dim());
+        // fill the datat of mean_stats in mean_stats_appy whose dim is equal
+        // with the dim of feature.
+        // the dim of feats = dim * num_frames;
+        for (int32 idx = 0; idx < feats->Dim() / dim; ++idx) {
+            SubVector<double> stats_tmp(mean_stats_apply.Data() + dim * idx,
+                                        dim);
+            stats_tmp.CopyFromVec(mean_stats);
+        }
+        offset.AddVec(-1.0 / count, mean_stats_apply);
+        feats->AddVec(1.0, offset);
+        return;
     }
-    scale = 1.0 / sqrt(var);
-    if (scale != scale || 1/scale == 0.0)
-      KALDI_ERR << "NaN or infinity in cepstral mean/variance computation";
-    offset = -(mean*scale);
-    for (int32 d_skip = d; d_skip < feats->Dim();) {
-      norm(0, d_skip) = offset;
-      norm(1, d_skip) = scale;
-      d_skip = d_skip + dim;
+    // norm(0, d) = mean offset;
+    // norm(1, d) = scale, e.g. x(d) <-- x(d)*norm(1, d) + norm(0, d).
+    kaldi::Matrix<BaseFloat> norm(2, feats->Dim());
+    for (int32 d = 0; d < dim; d++) {
+        double mean, offset, scale;
+        mean = stats_(0, d) / count;
+        double var = (stats_(1, d) / count) - mean * mean, floor = 1.0e-20;
+        if (var < floor) {
+            KALDI_WARN << "Flooring cepstral variance from " << var << " to "
+                       << floor;
+            var = floor;
+        }
+        scale = 1.0 / sqrt(var);
+        if (scale != scale || 1 / scale == 0.0)
+            KALDI_ERR
+                << "NaN or infinity in cepstral mean/variance computation";
+        offset = -(mean * scale);
+        for (int32 d_skip = d; d_skip < feats->Dim();) {
+            norm(0, d_skip) = offset;
+            norm(1, d_skip) = scale;
+            d_skip = d_skip + dim;
+        }
     }
-  }
-  // Apply the normalization.
-  feats->MulElements(norm.Row(1));
-  feats->AddVec(1.0, norm.Row(0));
+    // Apply the normalization.
+    feats->MulElements(norm.Row(1));
+    feats->AddVec(1.0, norm.Row(0));
 }
 
 void CMVN::ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats) {
-  ApplyCmvn(stats_, var_norm, feats);
+    ApplyCmvn(stats_, var_norm, feats);
 }
 
 bool CMVN::Compute(const VectorBase<BaseFloat>& input,
                    VectorBase<BaseFloat>* feat) const {
-                     return false;
+    return false;
 }
 
 
-} // namespace ppspeech
+}  // namespace ppspeech

speechx/speechx/frontend/normalizer.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 
 #pragma once
 
 #include "base/common.h"
 #include "frontend/feature_extractor_interface.h"
-#include "kaldi/util/options-itf.h"
 #include "kaldi/matrix/kaldi-matrix.h"
+#include "kaldi/util/options-itf.h"
 
 namespace ppspeech {
 
 struct DecibelNormalizerOptions {
-  float target_db;
-  float max_gain_db;
-  bool convert_int_float;
-  DecibelNormalizerOptions() :
-    target_db(-20),
-    max_gain_db(300.0),
-    convert_int_float(false) {}
+    float target_db;
+    float max_gain_db;
+    bool convert_int_float;
+    DecibelNormalizerOptions()
+        : target_db(-20), max_gain_db(300.0), convert_int_float(false) {}
 
     void Register(kaldi::OptionsItf* opts) {
-      opts->Register("target-db", &target_db, "target db for db normalization");
-      opts->Register("max-gain-db", &max_gain_db, "max gain db for db normalization");
-      opts->Register("convert-int-float", &convert_int_float, "if convert int samples to float");
+        opts->Register(
+            "target-db", &target_db, "target db for db normalization");
+        opts->Register(
+            "max-gain-db", &max_gain_db, "max gain db for db normalization");
+        opts->Register("convert-int-float",
+                       &convert_int_float,
+                       "if convert int samples to float");
     }
 };
 
 class DecibelNormalizer : public FeatureExtractorInterface {
   public:
     explicit DecibelNormalizer(const DecibelNormalizerOptions& opts);
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input);
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return dim_; }
     bool Compute(const kaldi::VectorBase<kaldi::BaseFloat>& input,
                  kaldi::VectorBase<kaldi::BaseFloat>* feat) const;
+
   private:
     DecibelNormalizerOptions opts_;
     size_t dim_;
@@ -43,7 +61,8 @@ class DecibelNormalizer : public FeatureExtractorInterface {
 class CMVN : public FeatureExtractorInterface {
   public:
     explicit CMVN(std::string cmvn_file);
-    virtual void AcceptWaveform(const kaldi::VectorBase<kaldi::BaseFloat>& input);
+    virtual void AcceptWaveform(
+        const kaldi::VectorBase<kaldi::BaseFloat>& input);
     virtual void Read(kaldi::VectorBase<kaldi::BaseFloat>* feat);
     virtual size_t Dim() const { return stats_.NumCols() - 1; }
     bool Compute(const kaldi::VectorBase<kaldi::BaseFloat>& input,
@@ -51,6 +70,7 @@ class CMVN : public FeatureExtractorInterface {
     // for test
     void ApplyCMVN(bool var_norm, kaldi::VectorBase<BaseFloat>* feats);
     void ApplyCMVNMatrix(bool var_norm, kaldi::MatrixBase<BaseFloat>* feats);
+
   private:
     kaldi::Matrix<double> stats_;
     std::shared_ptr<FeatureExtractorInterface> base_extractor_;

speechx/speechx/frontend/window.h

@@ -13,4 +13,3 @@
 // limitations under the License.
 
 // extract the window of kaldi feat.
-

speechx/speechx/nnet/decodable-itf.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 // itf/decodable-itf.h
 
 // Copyright 2009-2011  Microsoft Corporation;  Saarland University;
@@ -42,8 +56,10 @@ namespace kaldi {
 
     For online decoding, where the features are coming in in real time, it is
     important to understand the IsLastFrame() and NumFramesReady() functions.
-    There are two ways these are used: the old online-decoding code, in ../online/,
-    and the new online-decoding code, in ../online2/.  In the old online-decoding
+    There are two ways these are used: the old online-decoding code, in
+   ../online/,
+    and the new online-decoding code, in ../online2/.  In the old
+   online-decoding
     code, the decoder would do:
     \code{.cc}
     for (int frame = 0; !decodable.IsLastFrame(frame); frame++) {
@@ -52,13 +68,16 @@ namespace kaldi {
     \endcode
    and the call to IsLastFrame would block if the features had not arrived yet.
    The decodable object would have to know when to terminate the decoding.  This
-   online-decoding mode is still supported, it is what happens when you call, for
+   online-decoding mode is still supported, it is what happens when you call,
+   for
    example, LatticeFasterDecoder::Decode().
 
    We realized that this "blocking" mode of decoding is not very convenient
    because it forces the program to be multi-threaded and makes it complex to
-   control endpointing.  In the "new" decoding code, you don't call (for example)
-   LatticeFasterDecoder::Decode(), you call LatticeFasterDecoder::InitDecoding(),
+   control endpointing.  In the "new" decoding code, you don't call (for
+   example)
+   LatticeFasterDecoder::Decode(), you call
+   LatticeFasterDecoder::InitDecoding(),
    and then each time you get more features, you provide them to the decodable
    object, and you call LatticeFasterDecoder::AdvanceDecoding(), which does
    something like this:
@@ -68,7 +87,8 @@ namespace kaldi {
    }
    \endcode
    So the decodable object never has IsLastFrame() called.  For decoding where
-   you are starting with a matrix of features, the NumFramesReady() function will
+   you are starting with a matrix of features, the NumFramesReady() function
+   will
    always just return the number of frames in the file, and IsLastFrame() will
    return true for the last frame.
 
@@ -80,43 +100,52 @@ namespace kaldi {
    frame of the file once we've decided to terminate decoding.
 */
 class DecodableInterface {
- public:
-  /// Returns the log likelihood, which will be negated in the decoder.
-  /// The "frame" starts from zero.  You should verify that NumFramesReady() > frame
-  /// before calling this.
-  virtual BaseFloat LogLikelihood(int32 frame, int32 index) = 0;
-
-  /// Returns true if this is the last frame.  Frames are zero-based, so the
-  /// first frame is zero.  IsLastFrame(-1) will return false, unless the file
-  /// is empty (which is a case that I'm not sure all the code will handle, so
-  /// be careful).  Caution: the behavior of this function in an online setting
-  /// is being changed somewhat.  In future it may return false in cases where
-  /// we haven't yet decided to terminate decoding, but later true if we decide
-  /// to terminate decoding.  The plan in future is to rely more on
-  /// NumFramesReady(), and in future, IsLastFrame() would always return false
-  /// in an online-decoding setting, and would only return true in a
-  /// decoding-from-matrix setting where we want to allow the last delta or LDA
-  /// features to be flushed out for compatibility with the baseline setup.
-  virtual bool IsLastFrame(int32 frame) const = 0;
-
-  /// The call NumFramesReady() will return the number of frames currently available
-  /// for this decodable object.  This is for use in setups where you don't want the
-  /// decoder to block while waiting for input.  This is newly added as of Jan 2014,
-  /// and I hope, going forward, to rely on this mechanism more than IsLastFrame to
-  /// know when to stop decoding.
-  virtual int32 NumFramesReady() const {
-    KALDI_ERR << "NumFramesReady() not implemented for this decodable type.";
-    return -1;
-  }
-
-  /// Returns the number of states in the acoustic model
-  /// (they will be indexed one-based, i.e. from 1 to NumIndices();
-  /// this is for compatibility with OpenFst).
-  virtual int32 NumIndices() const = 0;
-
-  virtual std::vector<BaseFloat> FrameLogLikelihood(int32 frame) = 0;
-
-  virtual ~DecodableInterface() {}
+  public:
+    /// Returns the log likelihood, which will be negated in the decoder.
+    /// The "frame" starts from zero.  You should verify that NumFramesReady() >
+    /// frame
+    /// before calling this.
+    virtual BaseFloat LogLikelihood(int32 frame, int32 index) = 0;
+
+    /// Returns true if this is the last frame.  Frames are zero-based, so the
+    /// first frame is zero.  IsLastFrame(-1) will return false, unless the file
+    /// is empty (which is a case that I'm not sure all the code will handle, so
+    /// be careful).  Caution: the behavior of this function in an online
+    /// setting
+    /// is being changed somewhat.  In future it may return false in cases where
+    /// we haven't yet decided to terminate decoding, but later true if we
+    /// decide
+    /// to terminate decoding.  The plan in future is to rely more on
+    /// NumFramesReady(), and in future, IsLastFrame() would always return false
+    /// in an online-decoding setting, and would only return true in a
+    /// decoding-from-matrix setting where we want to allow the last delta or
+    /// LDA
+    /// features to be flushed out for compatibility with the baseline setup.
+    virtual bool IsLastFrame(int32 frame) const = 0;
+
+    /// The call NumFramesReady() will return the number of frames currently
+    /// available
+    /// for this decodable object.  This is for use in setups where you don't
+    /// want the
+    /// decoder to block while waiting for input.  This is newly added as of Jan
+    /// 2014,
+    /// and I hope, going forward, to rely on this mechanism more than
+    /// IsLastFrame to
+    /// know when to stop decoding.
+    virtual int32 NumFramesReady() const {
+        KALDI_ERR
+            << "NumFramesReady() not implemented for this decodable type.";
+        return -1;
+    }
+
+    /// Returns the number of states in the acoustic model
+    /// (they will be indexed one-based, i.e. from 1 to NumIndices();
+    /// this is for compatibility with OpenFst).
+    virtual int32 NumIndices() const = 0;
+
+    virtual std::vector<BaseFloat> FrameLogLikelihood(int32 frame) = 0;
+
+    virtual ~DecodableInterface() {}
 };
 /// @}
 }  // namespace Kaldi

speechx/speechx/nnet/decodable.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "nnet/decodable.h"
 
 namespace ppspeech {
@@ -5,51 +19,43 @@ namespace ppspeech {
 using kaldi::BaseFloat;
 using kaldi::Matrix;
 
-Decodable::Decodable(const std::shared_ptr<NnetInterface>& nnet):
-  frontend_(NULL),
-  nnet_(nnet),
-  finished_(false),
-  frames_ready_(0) {
-}
+Decodable::Decodable(const std::shared_ptr<NnetInterface>& nnet)
+    : frontend_(NULL), nnet_(nnet), finished_(false), frames_ready_(0) {}
 
 void Decodable::Acceptlikelihood(const Matrix<BaseFloat>& likelihood) {
-  frames_ready_ += likelihood.NumRows();
+    frames_ready_ += likelihood.NumRows();
 }
 
-//Decodable::Init(DecodableConfig config) {
+// Decodable::Init(DecodableConfig config) {
 //}
 
 bool Decodable::IsLastFrame(int32 frame) const {
-  CHECK_LE(frame, frames_ready_);
-  return finished_ && (frame == frames_ready_ - 1);
+    CHECK_LE(frame, frames_ready_);
+    return finished_ && (frame == frames_ready_ - 1);
 }
 
-int32 Decodable::NumIndices() const {
-  return 0;
-}
+int32 Decodable::NumIndices() const { return 0; }
 
-BaseFloat Decodable::LogLikelihood(int32 frame, int32 index) {
-  return 0;
-}
+BaseFloat Decodable::LogLikelihood(int32 frame, int32 index) { return 0; }
 
 void Decodable::FeedFeatures(const Matrix<kaldi::BaseFloat>& features) {
-  nnet_->FeedForward(features, &nnet_cache_); 
-  frames_ready_ += nnet_cache_.NumRows(); 
-  return ;
+    nnet_->FeedForward(features, &nnet_cache_);
+    frames_ready_ += nnet_cache_.NumRows();
+    return;
 }
 
 std::vector<BaseFloat> Decodable::FrameLogLikelihood(int32 frame) {
-  std::vector<BaseFloat> result;
-  result.reserve(nnet_cache_.NumCols());
-  for (int32 idx = 0; idx < nnet_cache_.NumCols(); ++idx) {
-    result[idx] = nnet_cache_(frame, idx); 
-  }
-  return result;
+    std::vector<BaseFloat> result;
+    result.reserve(nnet_cache_.NumCols());
+    for (int32 idx = 0; idx < nnet_cache_.NumCols(); ++idx) {
+        result[idx] = nnet_cache_(frame, idx);
+    }
+    return result;
 }
 
 void Decodable::Reset() {
-  // frontend_.Reset();
-  nnet_->Reset();
+    // frontend_.Reset();
+    nnet_->Reset();
 }
 
-} // namespace ppspeech
\ No newline at end of file
+}  // namespace ppspeech
\ No newline at end of file

speechx/speechx/nnet/decodable.h

-#include "nnet/decodable-itf.h"
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "base/common.h"
-#include "kaldi/matrix/kaldi-matrix.h"
 #include "frontend/feature_extractor_interface.h"
+#include "kaldi/matrix/kaldi-matrix.h"
+#include "nnet/decodable-itf.h"
 #include "nnet/nnet_interface.h"
 
 namespace ppspeech {
@@ -9,17 +23,20 @@ namespace ppspeech {
 struct DecodableOpts;
 
 class Decodable : public kaldi::DecodableInterface {
-  public: 
+  public:
     explicit Decodable(const std::shared_ptr<NnetInterface>& nnet);
-    //void Init(DecodableOpts config);
+    // void Init(DecodableOpts config);
     virtual kaldi::BaseFloat LogLikelihood(int32 frame, int32 index);
     virtual bool IsLastFrame(int32 frame) const;
     virtual int32 NumIndices() const;
     virtual std::vector<BaseFloat> FrameLogLikelihood(int32 frame);
-    void Acceptlikelihood(const kaldi::Matrix<kaldi::BaseFloat>& likelihood); // remove later
-    void FeedFeatures(const kaldi::Matrix<kaldi::BaseFloat>& feature); // only for test, todo remove later
+    void Acceptlikelihood(
+        const kaldi::Matrix<kaldi::BaseFloat>& likelihood);  // remove later
+    void FeedFeatures(const kaldi::Matrix<kaldi::BaseFloat>&
+                          feature);  // only for test, todo remove later
     void Reset();
     void InputFinished() { finished_ = true; }
+
   private:
     std::shared_ptr<FeatureExtractorInterface> frontend_;
     std::shared_ptr<NnetInterface> nnet_;

speechx/speechx/nnet/nnet_interface.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 
 #pragma once
 
@@ -10,10 +24,9 @@ namespace ppspeech {
 class NnetInterface {
   public:
     virtual void FeedForward(const kaldi::Matrix<kaldi::BaseFloat>& features,
-                             kaldi::Matrix<kaldi::BaseFloat>* inferences)= 0; 
+                             kaldi::Matrix<kaldi::BaseFloat>* inferences) = 0;
     virtual void Reset() = 0;
     virtual ~NnetInterface() {}
-
 };
 
 }  // namespace ppspeech

speechx/speechx/nnet/paddle_nnet.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "nnet/paddle_nnet.h"
 #include "absl/strings/str_split.h"
 
@@ -9,43 +23,44 @@ using std::shared_ptr;
 using kaldi::Matrix;
 
 void PaddleNnet::InitCacheEncouts(const ModelOptions& opts) {
-  std::vector<std::string> cache_names;
-  cache_names = absl::StrSplit(opts.cache_names, ",");
-  std::vector<std::string> cache_shapes;
-  cache_shapes = absl::StrSplit(opts.cache_shape, ",");
-  assert(cache_shapes.size() == cache_names.size());
-  
-  cache_encouts_.clear();
-  cache_names_idx_.clear();
-  for (size_t i = 0; i < cache_shapes.size(); i++) {
-    std::vector<std::string> tmp_shape;
-    tmp_shape = absl::StrSplit(cache_shapes[i], "-");
-    std::vector<int> cur_shape;
-    std::transform(tmp_shape.begin(), tmp_shape.end(),
-                    std::back_inserter(cur_shape),
-                    [](const std::string& s) {
-                        return atoi(s.c_str());
-                    });
-    cache_names_idx_[cache_names[i]] = i;
-    std::shared_ptr<Tensor<BaseFloat>> cache_eout = std::make_shared<Tensor<BaseFloat>>(cur_shape);
-    cache_encouts_.push_back(cache_eout);
-  }
+    std::vector<std::string> cache_names;
+    cache_names = absl::StrSplit(opts.cache_names, ",");
+    std::vector<std::string> cache_shapes;
+    cache_shapes = absl::StrSplit(opts.cache_shape, ",");
+    assert(cache_shapes.size() == cache_names.size());
+
+    cache_encouts_.clear();
+    cache_names_idx_.clear();
+    for (size_t i = 0; i < cache_shapes.size(); i++) {
+        std::vector<std::string> tmp_shape;
+        tmp_shape = absl::StrSplit(cache_shapes[i], "-");
+        std::vector<int> cur_shape;
+        std::transform(tmp_shape.begin(),
+                       tmp_shape.end(),
+                       std::back_inserter(cur_shape),
+                       [](const std::string& s) { return atoi(s.c_str()); });
+        cache_names_idx_[cache_names[i]] = i;
+        std::shared_ptr<Tensor<BaseFloat>> cache_eout =
+            std::make_shared<Tensor<BaseFloat>>(cur_shape);
+        cache_encouts_.push_back(cache_eout);
+    }
 }
 
-PaddleNnet::PaddleNnet(const ModelOptions& opts):opts_(opts) {
+PaddleNnet::PaddleNnet(const ModelOptions& opts) : opts_(opts) {
     paddle_infer::Config config;
     config.SetModel(opts.model_path, opts.params_path);
     if (opts.use_gpu) {
-      config.EnableUseGpu(500, 0);
+        config.EnableUseGpu(500, 0);
     }
     config.SwitchIrOptim(opts.switch_ir_optim);
     if (opts.enable_fc_padding == false) {
-      config.DisableFCPadding();
+        config.DisableFCPadding();
     }
     if (opts.enable_profile) {
-      config.EnableProfile();
+        config.EnableProfile();
     }
-    pool.reset(new paddle_infer::services::PredictorPool(config, opts.thread_num));
+    pool.reset(
+        new paddle_infer::services::PredictorPool(config, opts.thread_num));
     if (pool == nullptr) {
         LOG(ERROR) << "create the predictor pool failed";
     }
@@ -59,7 +74,7 @@ PaddleNnet::PaddleNnet(const ModelOptions& opts):opts_(opts) {
     vector<string> input_names_vec = absl::StrSplit(opts.input_names, ",");
     vector<string> output_names_vec = absl::StrSplit(opts.output_names, ",");
     paddle_infer::Predictor* predictor = GetPredictor();
-        
+
     std::vector<std::string> model_input_names = predictor->GetInputNames();
     assert(input_names_vec.size() == model_input_names.size());
     for (size_t i = 0; i < model_input_names.size(); i++) {
@@ -68,16 +83,14 @@ PaddleNnet::PaddleNnet(const ModelOptions& opts):opts_(opts) {
 
     std::vector<std::string> model_output_names = predictor->GetOutputNames();
     assert(output_names_vec.size() == model_output_names.size());
-    for (size_t i = 0;i < output_names_vec.size(); i++) {
+    for (size_t i = 0; i < output_names_vec.size(); i++) {
         assert(output_names_vec[i] == model_output_names[i]);
     }
     ReleasePredictor(predictor);
     InitCacheEncouts(opts);
 }
 
-void PaddleNnet::Reset() {
-  InitCacheEncouts(opts_);
-}
+void PaddleNnet::Reset() { InitCacheEncouts(opts_); }
 
 paddle_infer::Predictor* PaddleNnet::GetPredictor() {
     LOG(INFO) << "attempt to get a new predictor instance " << std::endl;
@@ -122,80 +135,88 @@ int PaddleNnet::ReleasePredictor(paddle_infer::Predictor* predictor) {
 }
 
 shared_ptr<Tensor<BaseFloat>> PaddleNnet::GetCacheEncoder(const string& name) {
-  auto iter = cache_names_idx_.find(name);
-  if (iter == cache_names_idx_.end()) {
-    return nullptr;
-  }
-  assert(iter->second < cache_encouts_.size());
-  return cache_encouts_[iter->second]; 
+    auto iter = cache_names_idx_.find(name);
+    if (iter == cache_names_idx_.end()) {
+        return nullptr;
+    }
+    assert(iter->second < cache_encouts_.size());
+    return cache_encouts_[iter->second];
 }
 
-void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features, Matrix<BaseFloat>* inferences) {
-  paddle_infer::Predictor* predictor = GetPredictor();
-  int row = features.NumRows();
-  int col = features.NumCols();
-  std::vector<BaseFloat> feed_feature;
-  // todo refactor feed feature: SmileGoat
-  feed_feature.reserve(row*col);
-  for (size_t row_idx = 0; row_idx < features.NumRows(); ++row_idx) {
-    for (size_t col_idx = 0; col_idx < features.NumCols(); ++col_idx) {
-      feed_feature.push_back(features(row_idx, col_idx));
+void PaddleNnet::FeedForward(const Matrix<BaseFloat>& features,
+                             Matrix<BaseFloat>* inferences) {
+    paddle_infer::Predictor* predictor = GetPredictor();
+    int row = features.NumRows();
+    int col = features.NumCols();
+    std::vector<BaseFloat> feed_feature;
+    // todo refactor feed feature: SmileGoat
+    feed_feature.reserve(row * col);
+    for (size_t row_idx = 0; row_idx < features.NumRows(); ++row_idx) {
+        for (size_t col_idx = 0; col_idx < features.NumCols(); ++col_idx) {
+            feed_feature.push_back(features(row_idx, col_idx));
+        }
+    }
+    std::vector<std::string> input_names = predictor->GetInputNames();
+    std::vector<std::string> output_names = predictor->GetOutputNames();
+    LOG(INFO) << "feat info: row=" << row << ", col= " << col;
+
+    std::unique_ptr<paddle_infer::Tensor> input_tensor =
+        predictor->GetInputHandle(input_names[0]);
+    std::vector<int> INPUT_SHAPE = {1, row, col};
+    input_tensor->Reshape(INPUT_SHAPE);
+    input_tensor->CopyFromCpu(feed_feature.data());
+    std::unique_ptr<paddle_infer::Tensor> input_len =
+        predictor->GetInputHandle(input_names[1]);
+    std::vector<int> input_len_size = {1};
+    input_len->Reshape(input_len_size);
+    std::vector<int64_t> audio_len;
+    audio_len.push_back(row);
+    input_len->CopyFromCpu(audio_len.data());
+
+    std::unique_ptr<paddle_infer::Tensor> h_box =
+        predictor->GetInputHandle(input_names[2]);
+    shared_ptr<Tensor<BaseFloat>> h_cache = GetCacheEncoder(input_names[2]);
+    h_box->Reshape(h_cache->get_shape());
+    h_box->CopyFromCpu(h_cache->get_data().data());
+    std::unique_ptr<paddle_infer::Tensor> c_box =
+        predictor->GetInputHandle(input_names[3]);
+    shared_ptr<Tensor<float>> c_cache = GetCacheEncoder(input_names[3]);
+    c_box->Reshape(c_cache->get_shape());
+    c_box->CopyFromCpu(c_cache->get_data().data());
+    bool success = predictor->Run();
+
+    if (success == false) {
+        LOG(INFO) << "predictor run occurs error";
     }
-  }
-  std::vector<std::string> input_names = predictor->GetInputNames();
-  std::vector<std::string> output_names = predictor->GetOutputNames();
-  LOG(INFO) << "feat info: row=" << row << ", col= " << col;
-
-  std::unique_ptr<paddle_infer::Tensor> input_tensor = predictor->GetInputHandle(input_names[0]);
-  std::vector<int> INPUT_SHAPE = {1, row, col};
-  input_tensor->Reshape(INPUT_SHAPE);
-  input_tensor->CopyFromCpu(feed_feature.data());
-  std::unique_ptr<paddle_infer::Tensor> input_len = predictor->GetInputHandle(input_names[1]);
-  std::vector<int> input_len_size = {1};
-  input_len->Reshape(input_len_size);
-  std::vector<int64_t> audio_len;
-  audio_len.push_back(row);
-  input_len->CopyFromCpu(audio_len.data());
-
-  std::unique_ptr<paddle_infer::Tensor> h_box = predictor->GetInputHandle(input_names[2]);
-  shared_ptr<Tensor<BaseFloat>> h_cache = GetCacheEncoder(input_names[2]);
-  h_box->Reshape(h_cache->get_shape());
-  h_box->CopyFromCpu(h_cache->get_data().data());
-  std::unique_ptr<paddle_infer::Tensor> c_box = predictor->GetInputHandle(input_names[3]);
-  shared_ptr<Tensor<float>> c_cache = GetCacheEncoder(input_names[3]);
-  c_box->Reshape(c_cache->get_shape());
-  c_box->CopyFromCpu(c_cache->get_data().data());
-  bool success = predictor->Run();
-
-  if (success == false) {
-      LOG(INFO) << "predictor run occurs error";
-  }
-
-  LOG(INFO) << "get the model success";
-  std::unique_ptr<paddle_infer::Tensor> h_out = predictor->GetOutputHandle(output_names[2]);
-  assert(h_cache->get_shape() == h_out->shape());
-  h_out->CopyToCpu(h_cache->get_data().data());
-  std::unique_ptr<paddle_infer::Tensor> c_out = predictor->GetOutputHandle(output_names[3]);
-  assert(c_cache->get_shape() == c_out->shape());
-  c_out->CopyToCpu(c_cache->get_data().data());
-
-  // get result
-  std::unique_ptr<paddle_infer::Tensor> output_tensor =
-      predictor->GetOutputHandle(output_names[0]);
-  std::vector<int> output_shape = output_tensor->shape();
-  row = output_shape[1];
-  col = output_shape[2];
-  vector<float> inferences_result;
-  inferences->Resize(row, col);
-  inferences_result.resize(row*col);
-  output_tensor->CopyToCpu(inferences_result.data());
-  ReleasePredictor(predictor);
-
-  for (int row_idx = 0; row_idx < row; ++row_idx) {
-    for (int col_idx = 0; col_idx < col; ++col_idx) {
-      (*inferences)(row_idx, col_idx) = inferences_result[col*row_idx + col_idx];
+
+    LOG(INFO) << "get the model success";
+    std::unique_ptr<paddle_infer::Tensor> h_out =
+        predictor->GetOutputHandle(output_names[2]);
+    assert(h_cache->get_shape() == h_out->shape());
+    h_out->CopyToCpu(h_cache->get_data().data());
+    std::unique_ptr<paddle_infer::Tensor> c_out =
+        predictor->GetOutputHandle(output_names[3]);
+    assert(c_cache->get_shape() == c_out->shape());
+    c_out->CopyToCpu(c_cache->get_data().data());
+
+    // get result
+    std::unique_ptr<paddle_infer::Tensor> output_tensor =
+        predictor->GetOutputHandle(output_names[0]);
+    std::vector<int> output_shape = output_tensor->shape();
+    row = output_shape[1];
+    col = output_shape[2];
+    vector<float> inferences_result;
+    inferences->Resize(row, col);
+    inferences_result.resize(row * col);
+    output_tensor->CopyToCpu(inferences_result.data());
+    ReleasePredictor(predictor);
+
+    for (int row_idx = 0; row_idx < row; ++row_idx) {
+        for (int col_idx = 0; col_idx < col; ++col_idx) {
+            (*inferences)(row_idx, col_idx) =
+                inferences_result[col * row_idx + col_idx];
+        }
     }
-  }
 }
 
-} // namespace ppspeech           
\ No newline at end of file
+}  // namespace ppspeech
\ No newline at end of file

speechx/speechx/nnet/paddle_nnet.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 
 #pragma once
 
-#include "nnet/nnet_interface.h"
 #include "base/common.h"
+#include "nnet/nnet_interface.h"
 #include "paddle_inference_api.h"
 
 #include "kaldi/matrix/kaldi-matrix.h"
@@ -13,71 +27,79 @@
 namespace ppspeech {
 
 struct ModelOptions {
-  std::string model_path;
-  std::string params_path;
-  int thread_num;
-  bool use_gpu;
-  bool switch_ir_optim;
-  std::string input_names;
-  std::string output_names;
-  std::string cache_names;
-  std::string cache_shape;
-  bool enable_fc_padding;
-  bool enable_profile;
-  ModelOptions() : 
-      model_path("../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdmodel"),
-      params_path("../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdiparams"),
-      thread_num(2),
-      use_gpu(false),
-      input_names("audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box"),
-      output_names("save_infer_model/scale_0.tmp_1,save_infer_model/scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/scale_3.tmp_1"),
-      cache_names("chunk_state_h_box,chunk_state_c_box"),
-      cache_shape("3-1-1024,3-1-1024"),
-      switch_ir_optim(false),
-      enable_fc_padding(false),
-      enable_profile(false) {
-  }
+    std::string model_path;
+    std::string params_path;
+    int thread_num;
+    bool use_gpu;
+    bool switch_ir_optim;
+    std::string input_names;
+    std::string output_names;
+    std::string cache_names;
+    std::string cache_shape;
+    bool enable_fc_padding;
+    bool enable_profile;
+    ModelOptions()
+        : model_path(
+              "../../../../model/paddle_online_deepspeech/model/"
+              "avg_1.jit.pdmodel"),
+          params_path(
+              "../../../../model/paddle_online_deepspeech/model/"
+              "avg_1.jit.pdiparams"),
+          thread_num(2),
+          use_gpu(false),
+          input_names(
+              "audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_"
+              "box"),
+          output_names(
+              "save_infer_model/scale_0.tmp_1,save_infer_model/"
+              "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
+              "scale_3.tmp_1"),
+          cache_names("chunk_state_h_box,chunk_state_c_box"),
+          cache_shape("3-1-1024,3-1-1024"),
+          switch_ir_optim(false),
+          enable_fc_padding(false),
+          enable_profile(false) {}
 
-  void Register(kaldi::OptionsItf* opts) {
-    opts->Register("model-path", &model_path, "model file path");
-    opts->Register("model-params", &params_path, "params model file path");
-    opts->Register("thread-num", &thread_num, "thread num");
-    opts->Register("use-gpu", &use_gpu, "if use gpu");
-    opts->Register("input-names", &input_names, "paddle input names");
-    opts->Register("output-names", &output_names, "paddle output names");
-    opts->Register("cache-names", &cache_names, "cache names");
-    opts->Register("cache-shape", &cache_shape, "cache shape");
-    opts->Register("switch-ir-optiom", &switch_ir_optim, "paddle SwitchIrOptim option");
-    opts->Register("enable-fc-padding", &enable_fc_padding, "paddle EnableFCPadding option");
-    opts->Register("enable-profile", &enable_profile, "paddle EnableProfile option");
-  }
+    void Register(kaldi::OptionsItf* opts) {
+        opts->Register("model-path", &model_path, "model file path");
+        opts->Register("model-params", &params_path, "params model file path");
+        opts->Register("thread-num", &thread_num, "thread num");
+        opts->Register("use-gpu", &use_gpu, "if use gpu");
+        opts->Register("input-names", &input_names, "paddle input names");
+        opts->Register("output-names", &output_names, "paddle output names");
+        opts->Register("cache-names", &cache_names, "cache names");
+        opts->Register("cache-shape", &cache_shape, "cache shape");
+        opts->Register("switch-ir-optiom",
+                       &switch_ir_optim,
+                       "paddle SwitchIrOptim option");
+        opts->Register("enable-fc-padding",
+                       &enable_fc_padding,
+                       "paddle EnableFCPadding option");
+        opts->Register(
+            "enable-profile", &enable_profile, "paddle EnableProfile option");
+    }
 };
 
-template<typename T>
+template <typename T>
 class Tensor {
-public:
-    Tensor() {
-    }
-    Tensor(const std::vector<int>& shape) :
-        _shape(shape) {
-        int data_size = std::accumulate(_shape.begin(), _shape.end(),
-                                            1, std::multiplies<int>());
+  public:
+    Tensor() {}
+    Tensor(const std::vector<int>& shape) : _shape(shape) {
+        int data_size = std::accumulate(
+            _shape.begin(), _shape.end(), 1, std::multiplies<int>());
         LOG(INFO) << "data size: " << data_size;
         _data.resize(data_size, 0);
     }
     void reshape(const std::vector<int>& shape) {
         _shape = shape;
-        int data_size = std::accumulate(_shape.begin(), _shape.end(),
-                                        1, std::multiplies<int>());
+        int data_size = std::accumulate(
+            _shape.begin(), _shape.end(), 1, std::multiplies<int>());
         _data.resize(data_size, 0);
     }
-    const std::vector<int>& get_shape() const {
-        return _shape;
-    }
-    std::vector<T>& get_data() {
-        return _data;
-    }
-private:
+    const std::vector<int>& get_shape() const { return _shape; }
+    std::vector<T>& get_data() { return _data; }
+
+  private:
     std::vector<int> _shape;
     std::vector<T> _data;
 };
@@ -85,15 +107,16 @@ private:
 class PaddleNnet : public NnetInterface {
   public:
     PaddleNnet(const ModelOptions& opts);
-    virtual void FeedForward(const kaldi::Matrix<kaldi::BaseFloat>& features, 
+    virtual void FeedForward(const kaldi::Matrix<kaldi::BaseFloat>& features,
                              kaldi::Matrix<kaldi::BaseFloat>* inferences);
     virtual void Reset();
-    std::shared_ptr<Tensor<kaldi::BaseFloat>> GetCacheEncoder(const std::string& name);
+    std::shared_ptr<Tensor<kaldi::BaseFloat>> GetCacheEncoder(
+        const std::string& name);
     void InitCacheEncouts(const ModelOptions& opts);
-    
+
   private:
     paddle_infer::Predictor* GetPredictor();
-    int ReleasePredictor(paddle_infer::Predictor* predictor); 
+    int ReleasePredictor(paddle_infer::Predictor* predictor);
 
     std::unique_ptr<paddle_infer::services::PredictorPool> pool;
     std::vector<bool> pool_usages;
@@ -107,4 +130,4 @@ class PaddleNnet : public NnetInterface {
     DISALLOW_COPY_AND_ASSIGN(PaddleNnet);
 };
 
-} // namespace ppspeech                                                                                                                                                                                                                             
+}  // namespace ppspeech

speechx/speechx/utils/file_utils.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "utils/file_utils.h"
 
 namespace ppspeech {
@@ -17,5 +31,4 @@ bool ReadFileToVector(const std::string& filename,
 
     return true;
 }
-
 }
\ No newline at end of file

speechx/speechx/utils/file_utils.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "base/common.h"
 
 namespace ppspeech {
 
-bool ReadFileToVector(const std::string& filename, 
-                      std::vector<std::string>* data); 
-
+bool ReadFileToVector(const std::string& filename,
+                      std::vector<std::string>* data);
 }